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@InProceedings{wang_etal_2015b,
  author    = {Hao Wang and Xingjian Shi and Dit{-}Yan Yeung},
  title     = {Relational Stacked Denoising Autoencoder for Tag Recommendation},
  booktitle = {Proceedings of the Twenty-Ninth {AAAI} Conference on Artificial Intelligence, January 25-30, 2015, Austin, Texas, {USA.}},
  year      = {2015},
  pages     = {3052--3058},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/aaai/WangSY15},
  crossref  = {DBLP:conf/aaai/2015},
  timestamp = {Thu, 14 Jul 2016 01:00:00 +0200},
  url       = {https://www.aaai.org/ocs/index.php/AAAI/AAAI15/paper/view/9350/9980},
}

@Article{budura_etal_2009,
  author        = {Budura, A. and Michel, S. and Cudré-Mauroux, P. and Aberer, K.},
  title         = {Neighborhood-based tag prediction},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2009},
  volume        = {5554 LNCS},
  pages         = {608-622},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {We consider the problem of tag prediction in collaborative tagging systems where users share and annotate resources on the Web. We put forward HAMLET, a novel approach to automatically propagate tags along the edges of a graph which relates similar documents. We identify the core principles underlying tag propagation for which we derive suitable scoring models combined in one overall ranking formula. Leveraging these scores, we present an efficient top-k tag selection algorithm that infers additional tags by carefully inspecting neighbors in the document graph. Experiments using real-world data demonstrate the viability of our approach in large-scale environments where tags are scarce. © 2009 Springer Berlin Heidelberg.},
  affiliation   = {Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland; MIT, United States},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-642-02121-3_45},
  keywords      = {rank3},
  source        = {Scopus},
  url           = {http://people.csail.mit.edu/pcm/papers/TagPrediction.pdf},
}

@Article{chen_etal_2007,
  author          = {Chen, A. and Chen, H.-H. and Huang, P.},
  title           = {Predicting social annotation by spreading activation},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2007},
  volume          = {4822 LNCS},
  pages           = {277-286},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Social bookmark services like del.icio.us enable easy annotation for users to organize their resources. Collaborative tagging provides useful index for information retrieval. However, lack of sufficient tags for the developing documents, in particular for new arrivals, hides important documents from being retrieved at the earlier stages. This paper proposes a spreading activation approach to predict social annotation based on document contents and users' tagging records. Total 28,792 mature documents selected from del.icio.us are taken as answer keys. The experimental results show that this approach predicts 71.28% of a 100 users' tag set with only 5 users' tagging records, and 84.76% of a 13-month tag set with only 1-month tagging record under the precision rates of 82.43% and 89.67%, respectively. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation     = {Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan; Department of Computer Science and Information Engineering, National Taiwan University, Taipei, Taiwan},
  author_keywords = {Collaborative tagging; Social annotation; Spreading activation},
  document_type   = {Conference Paper},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-540-77094-7_37},
}

@Conference{sereno_etal_2007,
  author          = {Sereno, B. and Shum, S.B. and Motta, E.},
  title           = {Formalization, user strategy and interaction design: Users' behaviour with discourse tagging semantics},
  year            = {2007},
  volume          = {273},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {When authors publish their interpretations of the ideas, opinions, claims or rebuttals in the literature, they are drawing on a repertoire of well understood moves, contributing to an extended discourse. Readers also bring their own perspective to documents, interpreting them in the light of their own research interests, and initiating, for instance, new connections that may not have been intended by authors. Collaborative, social, tagging holds promise as an approach to mediating these processes via the Web, but may lack the discourse dimension that is fundamental to the articulation of interpretations. We therefore take a hybrid semiformal approach to add structure to freeform folksonomies. Our experience demonstrates that this particular brand of tagging requires tools designed specifically for this sensemaking task by providing enough support to initiate the annotation, while not overwhelming users with suggestions. We describe a tool called ClaimSpotter that aims at supporting this tradeoff, through a novel combination of system-initiated tag recommendations, Web interface design, and an expanded conception of how tags can be both expressed, and semantically linked. We then report a detailed study which analysed the tool's usability and the tag structures created, contributing to our understanding of the implications of adding structure to collaborative tagging.},
  affiliation     = {Centre for Advanced Learning Technologies, INSEAD, Boulevard de Constance, F-77305 Fontainebleau, France; Knowledge Media Institute, Open University, Walton Hall, Milton Keynes, United Kingdom},
  author_keywords = {Argumentation; Discourse Relations; Pragmatic Web; Semantics; Sensemaking; Social tagging; Usability},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  keywords        = {rank1},
  page_count      = {10},
  source          = {Scopus},
  url             = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.449.4840&rep=rep1&type=pdf},
}

@Conference{oren_etal_2007,
  author        = {Oren, E. and Gerke, S. and Decker, S.},
  title         = {Simple algorithms for predicate suggestions using similarity and co-occurrence},
  year          = {2007},
  volume        = {4519 LNCS},
  pages         = {160-174},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {When creating Semantic Web data, users have to make a critical choice for a vocabulary: only through shared vocabularies can meaning be established. A centralised policy prevents terminology divergence but would restrict users needlessly. As seen in collaborative tagging environments, suggestion mechanisms help terminology convergence without forcing users. We introduce two domain-independent algorithms for recommending predicates (RDF statements) about resources, based on statistical dataset analysis. The first algorithm is based on similarity between resources, the second one is based on co-occurrence of predicates. Experimental evaluation shows very promising results: a high precision with relatively high recall in linear runtime performance. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation   = {Digital Enterprise Research Institute, National University of Ireland, Galway, Galway, Ireland},
  document_type = {Conference Paper},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007%2F978-3-540-72667-8_13},
}

@InProceedings{han_chen_2007,
  author        = {L. Han and G. Chen},
  title         = {HFCT: A Hybrid Fuzzy Clustering Method for Collaborative Tagging},
  booktitle     = {2007 International Conference on Convergence Information Technology (ICCIT 2007)},
  year          = {2007},
  pages         = {1389-1394},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {In recent years, there has been considerable interest in collaborative tagging. This paper proposes a hybrid fuzzy clustering method for collaborative tagging. Key feature of the method includes using a combination of the fuzzy c-means and the subtractive clustering to handle collaborative tagging problems. The method allows a resource to belong to more than one laggings with different membership grade. The HFCT method need not know in advance the number of laggings, in order to avoid the difficulty of initial guesses of the number of taggings.},
  doi           = {10.1109/ICCIT.2007.155},
  keywords      = {Internet;fuzzy set theory;groupware;pattern clustering;Web resource annotation;collaborative tagging problem;hybrid fuzzy clustering method;Clustering algorithms;Clustering methods;Collaborative software;Computer science;Fuzzy sets;Information technology;International collaboration;Ontologies;Organizing;Tagging},
}

@InProceedings{goh_etal_2008,
  author        = {D. H. L. Goh and C. S. Lee and A. Y. K. Chua and K. Razikin},
  title         = {An Examination of the Effectiveness of Social Tagging for Resource Discovery},
  booktitle     = {2008 International Workshop on Information-Explosion and Next Generation Search},
  year          = {2008},
  pages         = {23-30},
  month         = {April},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging allows users to assign keywords (tags) to resources facilitating their future access by the tag creator, and possibly by other users. In terms of its support for resource discovery, social tagging has both proponents and critics. The goal of this paper investigates if tags are an effective means for helping users locate useful resources. Adopting techniques from text categorization, we downloaded Web pages and their associated tags from del.icio.us, and trained Support Vector Machine classifiers to determine if the documents could be assigned to their associated tags. Results from the classifiers in terms of precision, recall and F1 score were mixed, suggesting that that not all tags could be used by public users for resource discovery. Detailed analyses of our results revealed characteristics of effective and ineffective tags for resource discovery. From these, implications for social tagging systems are discussed.},
  doi           = {10.1109/INGS.2008.11},
  keywords      = {Internet;classification;information retrieval;learning (artificial intelligence);support vector machines;text analysis;Web document;Web resource access;Web resource discovery;Web searching;Web site;keyword assignment;social tagging systems;tag creator;text categorization;trained support vector machine classifier;Cultural differences;Navigation;Organizing;Support vector machine classification;Support vector machines;Tagging;Taxonomy;Text categorization;Vocabulary;Web pages;Resource discovery;Social tagging;Support Vector Machines;Text categorization, rank1},
}

@InProceedings{graham_caverlee_2008,
  author        = {R. Graham and J. Caverlee},
  title         = {Exploring Feedback Models in Interactive Tagging},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {1},
  pages         = {141-147},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {One of the cornerstones of the Social Web is informal user-generated metadata (or tags) for annotating web objects like pages, images, and videos. However, many real-world domains are currently left out of the social tagging phenomenon due to the lack of a wide-scale tagging-savvy audience - domains like the personal desktop, enterprise intranets, and digital libraries. Hence in this paper, we propose a lightweight interactive tagging framework for providing high-quality tag suggestions for the vast majority of untagged content. One of the salient features of the proposed framework is its incorporation of user feedback for iteratively refining tag suggestions. Concretely, we describe and evaluate three feedback models - Tag-Based, Term-Based, and Tag Co-location. Through extensive user evaluation and testing, we find that feedback can significantly improve tag quality with minimal user involvement.},
  doi           = {10.1109/WIIAT.2008.419},
  keywords      = {feedback;identification technology;interactive systems;social networking (online);annotating Web objects;digital libraries;enterprise intranets;feedback models;high-quality tag suggestions;interactive tagging;personal desktop;social Web;social tagging phenomenon;tag co-location feedback;tag-based feedback;term-based feedback;user feedback;user-generated metadata;wide-scale tagging-savvy audience;Computer science;Feedback;Intelligent agent;Software libraries;Space exploration;Tagging;Testing;USA Councils;Videos;Web pages},
}

@InProceedings{abel_etal_2008,
  author        = {F. Abel and N. Henze and D. Krause},
  title         = {Exploiting Additional Context for Graph-Based Tag Recommendations in Folksonomy Systems},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {1},
  pages         = {148-154},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Folksonomy systems enable users to participate in the Web content creation process by annotating (tagging) resources with freely chosen keywords. Still, it is an open issue how to exploit this user-created content, and how to process and use these emergent semantics effectively. We investigate how the context of Web resources can be utilized to improve recommended strategies in social tagging systems. We focus on the GroupMe! system, which enables users to create groups in order to bundle Web resources. GroupMe! groups form valuable context information for the resources contained in such groups. In this paper we exploit graph-based tag recommendation strategies, evaluate them in the GroupMe! dataset, and benchmark our results against other approaches. In our evaluations we show that graph-based strategies outperform other approaches, and show the immanent benefit of graph-based recommendation strategies which exploit the group context for recommending tags to untagged resources.},
  doi           = {10.1109/WIIAT.2008.432},
  keywords      = {Internet;graph theory;groupware;information analysis;information retrieval;search engines;social networking (online);GroupMe! system;Web content creation process;folksonomy system;graph-based tag recommendation;social tagging system;Benchmark testing;Collaboration;Collaborative work;Databases;Intelligent agent;Semantic Web;Tagging;User-generated content;Videos;Web sites;folksonomy systems;tag recommendations},
}

@InProceedings{zhou_etal_2008,
  author        = {Ning Zhou and W. K. Cheung and Xiangyang Xue and Guoping Qiu},
  title         = {Collaborative and content-based image labeling},
  booktitle     = {2008 19th International Conference on Pattern Recognition},
  year          = {2008},
  pages         = {1-4},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Many on-line photo sharing systems allow users to tag their images so as to support semantic image search. In this paper, we study how one can take advantages of the already-tagged images to (semi-)automate the labeling of newly uploaded ones. In particular, we propose a hybrid approach for the prediction where user-provided tags and image visual contents are fused under a unified probabilistic framework. Kernel smoothing and collaborative filtering techniques are explored for improving the accuracy of the probabilistic models estimation. By comparing with some state-of-the-art content-based image labeling methods, we have empirically shown that 1) the proposed method can achieve comparable tag prediction accuracy when there is no user-provided tag, and that 2) it can significantly boost the prediction accuracy if the user can provide just a few tags.},
  doi           = {10.1109/ICPR.2008.4761473},
  issn          = {1051-4651},
  keywords      = {groupware;image processing;image retrieval;information filtering;probability;collaborative filtering technique;collaborative image labeling;content-based image labeling;image visual contents;kernel smoothing technique;online photo sharing systems;probabilistic framework;semantic image search;tag prediction accuracy;Accuracy;Collaboration;Computer science;Content based retrieval;Filtering;Image retrieval;Kernel;Labeling;Smoothing methods;Tagging},
}

@InProceedings{hsu_chen_2008,
  author        = {M. H. Hsu and H. H. Chen},
  title         = {Tag Normalization and Prediction for Effective Social Media Retrieval},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {1},
  pages         = {770-774},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper, we propose a tag normalization algorithm to unify the userspsila annotations. Meanwhile, we explore some general phenomena in a social annotation system and propose a supervised tag prediction model to predict the stabilized tag set of a resource, with feedback of a small amount of user annotation records. The experiments show that a large potion of the stabilized tag set is predicted, and it is feasible to reduce the requirement of sufficient user annotations in the applications of social annotations.},
  doi           = {10.1109/WIIAT.2008.92},
  keywords      = {Web services;information retrieval;social annotation system;social media retrieval;supervised tag prediction model;tag normalization algorithm;Application software;Computer science;Feedback;Frequency;Information retrieval;Intelligent agent;Power engineering and energy;Power system modeling;Predictive models;Uniform resource locators;prediction;social annotation;social media;tag normalization},
}

@InProceedings{frias-martinez_etal_2008,
  author        = {E. Frías-Martinez and M. Cebrián and A. Jaimes},
  title         = {A Study on the Granularity of User Modeling for Tag Prediction},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {1},
  pages         = {828-831},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {One of the characteristics of tag prediction mechanisms is that, typically, all user models are constructed with the same granularity. In this paper we hypothesize and empirically demonstrate that in order to increase tag prediction accuracy, the granularity of each user model has to be adapted to the level of usage of each particular user. We have constructed user models for tag prediction using association rules in Bibsonomy, a popular social bookmark and publication sharing system, at three granularity levels: (1) canonical, (2) stereotypical and (3) individual. Our experiments show that prediction accuracy improves if the level of granularity matches the level of participation of the user in the community (i.e., amount of tagging in Bibsonomy).},
  doi           = {10.1109/WIIAT.2008.67},
  keywords      = {data mining;information analysis;user modelling;Bibsonomy;association rule;canonical granularity level;individual granularity level;publication sharing system;social bookmark;stereotypical granularity level;tag prediction;user modeling;Accuracy;Association rules;Data mining;Information retrieval;Intelligent agent;Libraries;Predictive models;Tagging},
}

@Conference{subramanya_liu_2008,
  author          = {Subramanya, S.B. and Liu, H.},
  title           = {SocialTagger - Collaborative tagging for blogs in the long tail},
  year            = {2008},
  pages           = {19-26},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Social bookmarking is the process through which users share tags for online resources like blogs with others. Such collaborative tags provide valuable metadata for retrieval systems. While the successes of collaborative tagging systems have been demonstrated by popular websites like Del.icio.us, these sites cover only a small fraction of the available blogs on the web. The vast majority of the blogs are not available on any collaborative tagging system and are often tagged only by the authors. This lack of coverage of collaborative tags is a considerable roadblock in using the tag metadata in a web scale information retrieval system. To solve this problem we propose and implement a system to automatically recommend collaborative tags for a blog. The automatically generated tags will help to surface the blogs by making them available on social book marking sites and allow them to be easily discovered and potentially further tagged by a wider population. Copyright 2008 ACM.},
  affiliation     = {Department of Computer Science and Engineering, Arizona State University, Tempe, AZ 85287, United States},
  author_keywords = {Blogs; Collaborative tagging},
  document_type   = {Conference Paper},
  doi             = {10.1145/1458583.1458588},
  journal         = {International Conference on Information and Knowledge Management, Proceedings},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1458583.1458588},
}

@Conference{calefato_etal_2008,
  author          = {Calefato, F. and Gendarmi, D. and Lanubile, F.},
  title           = {Towards social semantic suggestive tagging},
  year            = {2008},
  volume          = {314},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The organization of the knowledge on the web is increasingly becoming a social task performed by online communities whose members share a common interest in classifying different types of information for a later retrieval. Collaborative tagging systems allow people to organize a set of resources of interest through unconstrained annotations based on free keywords commonly named tags. Suggestive tagging techniques support users in this organization process and have shown to be helpful also in fostering a quick convergence to a shared tag vocabulary. In this paper, we propose a tag recommender which relies on the content analysis of the resource to be tagged, as well as on the personal and collective tagging history. The main contribution of this work is a model which combines semantic content analysis methods with existing suggestive tagging techniques. The expected benefit is the improvement of the user experience in social bookmarking systems, and more generally in collaborative tagging systems.},
  affiliation     = {University of Bari, Dipartimento di Informatica, Via Orabona, 4, 70126 - Bari, Italy},
  author_keywords = {Collaborative tagging; Content analysis; Folksonomy; Recommender system; Semantic web; Social bookmaking; Suggestive tagging},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  page_count      = {9},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-314/40.pdf},
}

@Conference{marinho_schmidt-thieme_2008,
  author        = {Marinho, L.B. and Schmidt-Thieme, L.},
  title         = {Collaborative tag recommendations},
  year          = {2008},
  pages         = {533-540},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {With the increasing popularity of collaborative tagging systems, services that assist the user in the task of tagging, such as tag recommenders, are more and more required. Being the scenario similar to traditional recommender systems where nearest neighbor algorithms, better known as collaborative filtering, were extensively and successfully applied, the application of the same methods to the problem of tag recommendation seems to be a natural way to follow. However, it is necessary to take into consideration some particularities of these systems, such as the absence of ratings and the fact that two entity types in a rating scale correspond to three top level entity types, i.e., user, resources and tags. In this paper we cast the tag recommendation problem into a collaborative filtering perspective and starting from a view on the plain recommendation task without attributes, we make a ground evaluation comparing different tag recommender algorithms on real data.},
  affiliation   = {Information Systems and Machine Learning Lab (ISMLL), University of Hildesheim, Samelsonplatz 1, D-31141 Hildesheim, Germany},
  document_type = {Conference Paper},
  journal       = {Studies in Classification, Data Analysis, and Knowledge Organization},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007/978-3-540-78246-9_63},
}

@Conference{symeonidis_etal_2008,
  author        = {Symeonidis, P. and Nanopoulos, A. and Manolopoulos, Y.},
  title         = {Tag recommendations based on tensor dimensionality reduction},
  year          = {2008},
  pages         = {43-50},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize information items (songs, pictures, web links, products etc.). Collaborative tagging systems recommend tags to users based on what tags other users have used for the same items, aiming to develop a common consensus about which tags best describe an item. However, they fail to provide appropriate tag recommendations, because: (i) users may have different interests for an information item and (ii) information items may have multiple facets. In contrast to the current tag recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items and tags. These data is represented by a 3-order tensor, on which latent semantic analysis and dimensionality reduction is performed using the Higher Order Singular Value Decomposition (HOSVD) technique. We perform experimental comparison of the proposed method against two state-of-the-art tag recommendations algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision. © 2008 ACM.},
  affiliation   = {Department of Informatics, Aristotle University, Thessaloniki 54124, Greece},
  document_type = {Conference Paper},
  doi           = {10.1145/1454008.1454017},
  journal       = {RecSys'08: Proceedings of the 2008 ACM Conference on Recommender Systems},
  keywords      = {rank2},
  source        = {Scopus},
  url           = {http://dl.acm.org/ft_gateway.cfm?id=1454017&type=pdf&CFID=975119194&CFTOKEN=74174692},
}

@Article{naaman_nair_2008,
  author        = {M. Naaman and R. Nair},
  title         = {ZoneTag's Collaborative Tag Suggestions: What is This Person Doing in My Phone?},
  journal       = {IEEE MultiMedia},
  year          = {2008},
  volume        = {15},
  number        = {3},
  pages         = {34-40},
  month         = {July},
  issn          = {1070-986X},
  __markedentry = {[felipe:1]},
  abstract      = {We describe ZoneTag, a camera phone application that allows users to capture, annotate, and share photos directly from their phone. We describe the simple mechanism for deriving tag suggestions and the ensuing interaction design for presenting these suggestions to the user. We also discuss quantitative and qualitative results from an 18-months deployment of ZoneTag, emphasizing the way people use and understand tag suggestions. In addition, we highlight several emerging issues that could play an important role in the collaborative tagging for multimedia as well as other resources. While the quantitative study on the use of the suggested tags feature implies clear benefits for tag suggestions, a set of qualitative studies imply that while tag suggestions are helpful, there are multiple issues that arise and require careful consideration.},
  doi           = {10.1109/MMUL.2008.69},
  keywords      = {cameras;groupware;identification technology;mobile computing;mobile handsets;multimedia computing;ZoneTag;camera phone application;collaborative tag suggestions;interaction design;multimedia;Application software;Bridges;Cameras;Collaboration;Collaborative work;Cultural differences;Drives;Home computing;Prototypes;Tagging},
}

@Article{yamamoto_etal_2008,
  author        = {D. Yamamoto and T. Masuda and S. Ohira and K. Nagao},
  title         = {Video Scene Annotation Based on Web Social Activities},
  journal       = {IEEE MultiMedia},
  year          = {2008},
  volume        = {15},
  number        = {3},
  pages         = {22-32},
  month         = {July},
  issn          = {1070-986X},
  __markedentry = {[felipe:1]},
  abstract      = {This article describes a mechanism to acquire the semantics of video content from the activities of Web communities that use a bulletin-board system and Weblog tools to discuss video scenes.},
  doi           = {10.1109/MMUL.2008.67},
  keywords      = {Internet;multimedia computing;Web communities;Web social activities;Weblog tools;bulletin-board system;video content semantics;video scene annotation;Automatic speech recognition;Content based retrieval;Costs;Data mining;Image recognition;Layout;MPEG 7 Standard;Pattern recognition;Robustness;Video sharing},
}

@InProceedings{godoy_amandi_2008,
  author        = {Godoy, D. and Amandi, A.},
  title         = {Hybrid content and tag-based profiles for recommendation in Collaborative tagging systems},
  booktitle     = {2008 Latin American Web Conference},
  year          = {2008},
  pages         = {58-65},
  month         = {Oct},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging systems have grown in popularity over the Web in the last years on account of their simplicity to categorize and retrieve content using open-ended tags. The increasing number of users providing information about themselves through social tagging activities caused the emergence of tag-based profiling approaches, which assume that users expose their preferences for certain contents through tag assignments. On the other hand, numerous content-based profiling techniques have been developed to address the problem of obtaining accurate models of user information preferences in order to assist users with information-related tasks such as Web browsing or searching. In this paper we propose a hybrid user profiling strategy that takes advantage of both content-based profiles describing long-term information interests that a recommender system can acquired along time and interests revealed through tagging activities, with the goal of enhancing the interaction of users with a collaborative tagging system. Experimental results of using hybrid profiles for tag recommendation are reported and possible applications of these profiles for obtaining personalized recommendations in collaborative tagging systems are discussed. © 2008 IEEE.},
  affiliation   = {ISISTAN Research Institute, UNICEN University, Campus Universitario, Paraje Arroyo Seco CP 7000, Tandil, Bs. As., Argentina; CONICET, Consejo Nacional de Investigaciones Científicas y Técnicas, Argentina},
  art_number    = {4756162},
  document_type = {Conference Paper},
  doi           = {10.1109/LA-WEB.2008.15},
  journal       = {Proceedings of the Latin American Web Conference, LA-WEB 2008},
  keywords      = {Internet;content-based retrieval;user modelling;Web browsing;World Wide Web;collaborative tagging systems;content retrieval;hybrid content;hybrid user profiling;open-ended tags;personalized recommendations;recommender system;social tagging activity;tag assignments;tag recommendation;tag-based profiling;user information preferences;Collaboration;Collaborative work;Content based retrieval;Frequency;History;Information services;Recommender systems;Tagging;Web pages;Web sites;Web 2.0;collaborative tagging systems;user profiling},
  source        = {Scopus},
  url           = {http://ieeexplore.ieee.org/document/4756162/},
}

@InProceedings{baruzzo_etal_2009,
  author        = {A. Baruzzo and A. Dattolo and N. Pudota and C. Tasso},
  title         = {Recommending New Tags Using Domain-Ontologies},
  booktitle     = {2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2009},
  volume        = {3},
  pages         = {409-412},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Tagging is a representative activity of social Web, useful for organizing information into knowledge. This activity presents some open issues, due in the majority to the manual insertion of tags. On the other hand, domain ontology is a specification of the conceptualization of a domain in terms of concepts, attributes and relations. Domain ontologies have a good potential to improve information organization, management and understanding. In this paper, we propose an automated approach for recommending new tags for Web resources by using domain ontologies and key-phrases. The proposed approach is implemented in the PIRATES framework, a prototype system for personalized content retrieval, annotation, and classification. Our approach is then explained with a simple use-case scenario.},
  doi           = {10.1109/WI-IAT.2009.313},
  keywords      = {Computer science;Conferences;Content based retrieval;Intelligent agent;Mathematics;Ontologies;Organizing;Tagging;User-generated content;Vocabulary},
}

@InProceedings{awawdeh_anderson_2009,
  author        = {R. Awawdeh and T. Anderson},
  title         = {Improved Search in Tag-Based Systems},
  booktitle     = {2009 Ninth International Conference on Intelligent Systems Design and Applications},
  year          = {2009},
  pages         = {288-293},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Social bookmarking systems are used by millions of Web users to tag, save and share items. User-defined tags, however, are so variable in quality that searching on these tags alone is ineffective. One way to improve search in bookmarking systems is by adding more metadata to the user-defined tags to enhance tag quality. Such an approach would add value by incorporating information about the content of the resource while retaining the original user-defined tag. Tags automatically extracted from the resource could be the main source for tag enhancement. This paper describes how users' tags can be enhanced with metadata in the form of additional tags automatically extracted from the original document. An evaluation study shows how the enhanced tag set improved user searching in comparison to using only user-defined tags.},
  doi           = {10.1109/ISDA.2009.170},
  issn          = {2164-7143},
  keywords      = {Internet;meta data;query formulation;Internet;metadata;social bookmarking systems;tag quality enhancement;user-defined tags;Collaborative software;Collaborative work;Control systems;Data mining;Guidelines;Intelligent systems;Mathematics;Prototypes;Search engines;Vocabulary;Folksonomy;Tag-Search;Tagging},
}

@Article{li_etal_2009,
  author        = {X. Li and C. G. M. Snoek and M. Worring},
  title         = {Learning Social Tag Relevance by Neighbor Voting},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2009},
  volume        = {11},
  number        = {7},
  pages         = {1310-1322},
  month         = {Nov},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Social image analysis and retrieval is important for helping people organize and access the increasing amount of user tagged multimedia. Since user tagging is known to be uncontrolled, ambiguous, and overly personalized, a fundamental problem is how to interpret the relevance of a user-contributed tag with respect to the visual content the tag is describing. Intuitively, if different persons label visually similar images using the same tags, these tags are likely to reflect objective aspects of the visual content. Starting from this intuition, we propose in this paper a neighbor voting algorithm which accurately and efficiently learns tag relevance by accumulating votes from visual neighbors. Under a set of well-defined and realistic assumptions, we prove that our algorithm is a good tag relevance measurement for both image ranking and tag ranking. Three experiments on 3.5 million Flickr photos demonstrate the general applicability of our algorithm in both social image retrieval and image tag suggestion. Our tag relevance learning algorithm substantially improves upon baselines for all the experiments. The results suggest that the proposed algorithm is promising for real-world applications.},
  doi           = {10.1109/TMM.2009.2030598},
  keywords      = {image classification;image retrieval;learning (artificial intelligence);Flickr photo;image ranking;learning social tag relevance;neighbor voting algorithm;social image analysis;social image retrieval;tag ranking;tag relevance learning algorithm;user tagged multimedia;visual content;visual neighbor;Multimedia indexing and retrieval;neighbor voting;social tagging;tag relevance learning},
}

@InProceedings{choi_etal_2009,
  author        = {J. Choi and W. De Neve and Y. M. Ro and K. N. Plataniotis},
  title         = {Face annotation for personal photos using collaborative face recognition in online social networks},
  booktitle     = {2009 16th International Conference on Digital Signal Processing},
  year          = {2009},
  pages         = {1-8},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic face annotation (or tagging) facilitates improved retrieval and organization of personal photos in online social networks. In this paper, we present a new collaborative face recognition (FR) method that aims to improve face annotation accuracy. The proposed method makes efficient use of multiple FR engines and databases that are distributed over an online social network. The performance of our collaborative face recognition method was successfully evaluated using the standard MPEG-7 VCE-3 data set and a set of real-world personal photos from the Web. The efficacy of the proposed method is demonstrated in terms of comparative annotation performance against non-collaborative approaches utilizing a single FR engine and a single database only.},
  doi           = {10.1109/ICDSP.2009.5201095},
  issn          = {1546-1874},
  keywords      = {face recognition;groupware;social networking (online);video coding;video retrieval;World Wide Web;automatic face annotation;collaborative face recognition method;distributed database;multiple FR engine;noncollaborative approach;online social network;personal photo organization;personal photo retrieval;social tagging;standard MPEG-7 VCE-3 data set;Collaboration;Evidence fusion;Face annotation;Personal photos;Social context},
}

@InProceedings{lin_etal_2009,
  author        = {Y. R. Lin and H. Sundaram and M. De Choudhury and A. Kelliher},
  title         = {Temporal patterns in social media streams: Theme discovery and evolution using joint analysis of content and context},
  booktitle     = {2009 IEEE International Conference on Multimedia and Expo},
  year          = {2009},
  pages         = {1456-1459},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Online social networking sites such as Flickr and Facebook provide a diverse range of functionalities that foster online communities to create and share media content. In particular, Flickr groups are increasingly used to aggregate and share photos about a wide array of topics or themes. Unlike photo repositories where images are typically organized with respect to static topics, the photo sharing process as in Flickr often results in complex time-evolving social and visual patterns. Characterizing such time-evolving patterns can enrich media exploring experience in a social media repository. In this paper, we propose a novel framework that characterizes distinct time-evolving patterns of group photo streams. We use a nonnegative joint matrix factorization approach to incorporate image content features and contextual information, including associated tags, photo owners and post times. In our framework, we consider a group as a mixture of themes - each theme exhibits similar patterns of image content and context. The theme extraction is to best explain the observed image content features and associations with tags, users and times. Extensive experiments on a Flickr dataset suggest that our approach is able to extract meaningful evolutionary patterns from group photo streams. We evaluate our method through a tag prediction task. Our prediction results outperform baseline methods, which indicate the utility of our theme based joint analysis.},
  doi           = {10.1109/ICME.2009.5202777},
  issn          = {1945-7871},
  keywords      = {computer vision;evolutionary computation;matrix decomposition;social networking (online);Flickr dataset;evolutionary patterns;image content features;image contextual information;nonnegative joint matrix factorization approach;online social networking sites;social media repository;social media streams;temporal patterns;theme discovery;theme evolution;Aggregates;Art;Bars;Data mining;Facebook;Intelligent networks;Pattern analysis;Social network services;Streaming media;Text mining, rank1},
}

@InProceedings{sun_etal_2009,
  author        = {K. Sun and L. Lin and B. Liu and C. Sun and X. Wang},
  title         = {Foxinfo1.0: A Chinese Topic-Oriented Search Engine},
  booktitle     = {2009 International Conference on Asian Language Processing},
  year          = {2009},
  pages         = {91-96},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Topic-oriented search engine (topic-search) is a new IR service which provides compounded types of information with certain user queried topic in one page. It firstly categorizes user query into a certain domain, and then organizes several types of information based on the query keywords into a magazine-style topic page for user. In this paper, we propose a Chinese topic-oriented search engine service, named as Foxinfo1.0, which provides a 360 degree view of the topic that interests the users who're seeking it. Different from the original topic-search which employs the keyword-based topic to retrieve and aggregate relevant information, Foxinfo1.0 could organize information from different abstraction level by employing tag to describe the topics of queries and information. Further, in order to predict tags from queries and web documents, a tag prediction algorithm named as CTAG is proposed, which could concern tags from different levels, and performs much better than the baseline method like AutoTag.},
  doi           = {10.1109/IALP.2009.28},
  keywords      = {information retrieval;search engines;AutoTag;Chinese topic-oriented search engine;Foxinfol.O;information retrieval service;magazine-style topic page;query keywords;web documents;Aggregates;Blogs;Computer science;Information retrieval;Natural languages;Navigation;Prediction algorithms;Search engines;Sun;Tagging;CTAG;Tag Recommendation;Topic-oriented Search Engine},
}

@InProceedings{yang_etal_2009,
  author        = {K. Yang and M. Wang and H. J. Zhang},
  title         = {Active tagging for image indexing},
  booktitle     = {2009 IEEE International Conference on Multimedia and Expo},
  year          = {2009},
  pages         = {1620-1623},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Concept labeling and ontology-free tagging are the two typical manners of image annotation. Despite extensive research efforts have been dedicated to labeling, currently automatic image labeling algorithms are still far from satisfactory, and meanwhile manual labeling is rather labor-intensive. In contrast with labeling, tagging works in a free way and therefore it has better user experience for annotators. In this paper, we introduce an active tagging scheme that combines human and computer to assign tags to images. The scheme works in an iterative way. In each round, the most informative images are selected for manual tagging, and the remained images can be annotated by a tag prediction component. We have integrated multiple criteria for sample selection, including ambiguity, citation, and diversity. Experiments are conducted on different datasets and empirical results have demonstrated the effectiveness of the proposed approach.},
  doi           = {10.1109/ICME.2009.5202829},
  issn          = {1945-7871},
  keywords      = {computer vision;indexing;ontologies (artificial intelligence);active tagging scheme;automatic image labeling algorithm;image annotation;image indexing;integrated multiple criteria;ontology-free tagging;tag prediction component;Asia;Humans;Image storage;Indexing;Internet;Labeling;Large-scale systems;Ontologies;Tagging;YouTube;Tagging;active learning},
}

@InProceedings{guillaumin_etal_2009,
  author        = {M. Guillaumin and T. Mensink and J. Verbeek and C. Schmid},
  title         = {TagProp: Discriminative metric learning in nearest neighbor models for image auto-annotation},
  booktitle     = {2009 IEEE 12th International Conference on Computer Vision},
  year          = {2009},
  pages         = {309-316},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Image auto-annotation is an important open problem in computer vision. For this task we propose TagProp, a discriminatively trained nearest neighbor model. Tags of test images are predicted using a weighted nearest-neighbor model to exploit labeled training images. Neighbor weights are based on neighbor rank or distance. TagProp allows the integration of metric learning by directly maximizing the log-likelihood of the tag predictions in the training set. In this manner, we can optimally combine a collection of image similarity metrics that cover different aspects of image content, such as local shape descriptors, or global color histograms. We also introduce a word specific sigmoidal modulation of the weighted neighbor tag predictions to boost the recall of rare words. We investigate the performance of different variants of our model and compare to existing work. We present experimental results for three challenging data sets. On all three, TagProp makes a marked improvement as compared to the current state-of-the-art.},
  doi           = {10.1109/ICCV.2009.5459266},
  issn          = {1550-5499},
  keywords      = {image processing;learning (artificial intelligence);TagProp;computer vision;discriminative metric learning;image auto-annotation;image similarity metrics;tag predictions;weighted nearest-neighbor model;word specific sigmoidal modulation;Computer vision;Content management;Histograms;Large-scale systems;Nearest neighbor searches;Predictive models;Shape;Testing;Video sharing;Vocabulary},
}

@InProceedings{sarmento_etal_2009,
  author        = {L. Sarmento and S. Nunes and J. Teixeira and E. Oliveira},
  title         = {Propagating Fine-Grained Topic Labels in News Snippets},
  booktitle     = {2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2009},
  volume        = {3},
  pages         = {515-518},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {We propose an unsupervised method for propagating automatically extracted fine-grained topic labels among news items to improve their topic description for subsequent text classification procedure. This method compares vector representations of news items and assigns to each news item the label of its closest neighbour with a different topic label. Results obtained show that high precision can be achieved in propagating the top ranked topic label, and that 2-gram and 3-gram feature representations optimize the precision.},
  doi           = {10.1109/WI-IAT.2009.338},
  keywords      = {Clustering algorithms;Conferences;Feeds;Geography;Intelligent agent;Labeling;Marine vehicles;Oceans;Sun;Text categorization},
}

@Article{meo_etal_2009,
  author        = {Pasquale De Meo and Giovanni Quattrone and Domenico Ursino},
  title         = {Exploitation of semantic relationships and hierarchical data structures to support a user in his annotation and browsing activities in folksonomies},
  journal       = {Information Systems},
  year          = {2009},
  volume        = {34},
  number        = {6},
  pages         = {511 - 535},
  issn          = {0306-4379},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.is.2009.02.004},
  keywords      = {Folksonomies, Social tagging, Tag similarity, Social annotation},
  url           = {http://www.sciencedirect.com/science/article/pii/S030643790900009X},
}

@InProceedings{cao_etal_2009,
  author        = {Cao, Hao and Xie, Maoqiang and Xue, Lian and Liu, Chunhua and Teng, Fei and Huang, Yalou},
  title         = {Social Tag Prediction Base on Supervised Ranking Model},
  booktitle     = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year          = {2009},
  series        = {ECMLPKDDDC'09},
  pages         = {35--48},
  address       = {Aachen, Germany, Germany},
  publisher     = {CEUR-WS.org},
  __markedentry = {[felipe:1]},
  acmid         = {3056150},
  location      = {Bled, Slovenia},
  numpages      = {14},
  url           = {http://ceur-ws.org/Vol-497/paper_03.pdf},
}

@Conference{auyeung_etal_2009,
  author          = {Au Yeung, C.-M. and Gibbins, N. and Shadbolt, N.},
  title           = {User-induced links in collaborative tagging systems},
  year            = {2009},
  pages           = {787-796},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems allow users to use tags to describe their favourite online documents. Two documents that are maintained in the collection of the same user and/or assigned similar sets of tags can be considered as related from the perspective of the user, even though they may not be connected by hyperlinks. We call this kind of implicit relations user-induced links between documents. We consider two methods of identifying user-induced links in collaborative tagging, and compare these links with existing hyperlinks on the Web. Our analyses show that user-induced links have great potentials to enrich the existing link structure of the Web. We also propose to use these links as a basis for predicting how documents would be tagged. Our experiments show that they achieve much higher accuracy than existing hyperlinks. This study suggests that by studying the collective behaviour of users we are able to enhance navigation and organisation of Web documents. Copyright 2009 ACM.},
  affiliation     = {University of Southampton, Southampton, SO17 1BJ, United Kingdom},
  author_keywords = {Collaborative tagging; Folksonomy; Hyperlink},
  document_type   = {Conference Paper},
  doi             = {10.1145/1645953.1646053},
  journal         = {International Conference on Information and Knowledge Management, Proceedings},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-74549153304&doi=10.1145%2f1645953.1646053&partnerID=40&md5=9efd6f75550f47f6f2b07537d6869c2d},
}

@Conference{lipczak_etal_2009,
  author        = {Lipczak, M. and Hu, Y. and Kollet, Y. and Milios, E.},
  title         = {Tag sources for recommendation in collaborative tagging systems},
  year          = {2009},
  volume        = {497},
  pages         = {157-172},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging systems are social data repositories, in which users manage resources using descriptive keywords (tags). An important element of collaborative tagging systems is the tag recommender, which proposes a set of tags to each newly posted resource. In this paper we discuss the potential role of three tag sources: resource content as well as resource and user profiles in the tag recommendation system. Our system compiles a set of resource specific tags, which includes tags related to the title and tags previously used to describe the same resource (resource profile). These tags are checked against user profile tags - a rich, but imprecise source of information about user interests. The result is a set of tags related both to the resource and user. Depending on the character of processed posts this set can be an extension of the common tag recommendation sources, namely resource title and resource profile. The system was submitted to ECML PKDD Discovery Challenge 2009 for "content-based" and "graph-based" recommendation tasks, in which it took the first and third place respectively.},
  affiliation   = {Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada},
  document_type = {Conference Paper},
  journal       = {CEUR Workshop Proceedings},
  keywords      = {rank2},
  source        = {Scopus},
  url           = {http://ceur-ws.org/Vol-497/paper_19.pdf},
}

@Conference{musto_etal_2009,
  author          = {Musto, C. and Narducci, F. and De Gemmis, M. and Lops, P. and Semeraro, G.},
  title           = {A tag recommender system exploiting user and community behavior},
  year            = {2009},
  volume          = {532},
  pages           = {25-32},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Nowadays Web sites tend to be more and more social: users can upload any kind of information on collaborative platforms and can express their opinions about the content they enjoyed through textual feedbacks or reviews. These platforms allow users to annotate resources they like through freely chosen keywords (called tags). The main advantage of these tools is that they perfectly fit user needs, since the use of tags allows organizing the information in a way that closely follows the user mental model, making retrieval of information easier. However, the heterogeneity characterizing the communities causes some problems in the activity of social tagging: someone annotates resources with very specific tags, other people with generic ones, and so on. These drawbacks reduce the exploitation of collaborative tagging systems for retrieval and filtering tasks. Therefore, systems that assist the user in the task of tagging are required. The goal of these systems, called tag recommenders, is to suggest a set of relevant keywords for the resources to be annotated. This paper presents a tag recommender system called STaR (Social Tag Recommender system). Our system is based on two assumptions: 1) the more two or more resources are similar, the more they share common tags 2) a tag recommender should be able to exploit tags the user already used in order to extract useful keywords to label new resources. We also present an experimental evaluation carried out using a large dataset gathered from Bibsonomy.},
  affiliation     = {Dept. of Computer Science, University of Bari Aldo Moro, Italy},
  author_keywords = {Collaborative tagging systems; Folksonomies; Recommender systems; Web 2.0},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-532/paper4.pdf},
}

@Conference{gemmell_etal_2009,
  author          = {Gemmell, J. and Ramezani, M. and Schimoler, T. and Christiansen, L. and Mobasher, B.},
  title           = {A fast effective multi-channeled tag recommender},
  year            = {2009},
  volume          = {497},
  pages           = {59-70},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging applications allow users to annotate online resources, resulting in a complex three dimensional network of interrelated users, resources and tags often called a folksonom A pivotal challenge of these systems remains the inclusion of the varied information channels introduced by the multi-dimensional folksonomy into recommendation techniques. In this paper we propose a composite tag recommender based upon popularity and collaborative filtering. These recommenders were chosen based on their speed, memory requirements and ability to cover complimentary channels of the folksonomy. Alone these recommenders perform poorly; together they achieve a synergy which proves to be as effective as state of the art tag recommenders.},
  affiliation     = {Center for Web Intelligence School of Computing, DePaul University, Chicago, IL, United States},
  author_keywords = {Folksonomies; Hybrid Recommenders; Tag Recommenders},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-497/paper_25.pdf},
}

@Conference{gemmell_etal_2009a,
  author        = {Gemmell, J. and Schimoler, T. and Ramezani, M. and Christiansen, L. and Mobasher, B.},
  title         = {Improving folkrank with item-based collaborative filtering},
  year          = {2009},
  volume        = {532},
  pages         = {17-24},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging applications allow users to annotate online resources. The result is a complex tapestry of interrelated users, resources and tags often called a folksonomy. Folksonomies present an attractive target for data mining applications such as tag recommenders. A challenge of tag recommendation remains the adaptation of traditional recommendation techniques originally designed to work with two dimensional data. To date the most successful recommenders have been graph based approaches which explicitly connects all three components of the folksonomy. In this paper we speculate that graph based tag recommendation can be improved by coupling it with item-based collaborative filtering. We motive this hypothesis with a discussion of informational channels in folksonomies and provide a theoretical explanation of the additive potential for item-based collaborative filtering. We then provided experimental results on hybrid tag recommenders built from graph models and other techniques based on popularity, user-based collaborative filtering and item-based collaborative filtering. We demonstrate that a hybrid recommender built from a graph based model and item-based collaborative filtering outperforms its constituent recommenders. Furthermore the inability of the other recommenders to improve upon the graph-based approach suggests that they offer information already included in the graph based model. These results confirm our conjecture. We provide extensive evaluation of the hybrids using data collected from three real world collaborative tagging applications.},
  affiliation   = {Center for Web Intelligence, School of Computing, DePaul University, Chicago, IL, United States},
  document_type = {Conference Paper},
  journal       = {CEUR Workshop Proceedings},
  source        = {Scopus},
  url           = {http://ceur-ws.org/Vol-532/paper3.pdf},
}

@Conference{gemmell_etal_2009b,
  author        = {Gemmell, J. and Schimoler, T. and Ramezani, M. and Mobasher, B.},
  title         = {Adapting K-Nearest Neighbor for tag recommendation in Folksonomies},
  year          = {2009},
  volume        = {528},
  pages         = {75-80},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Folksonomies, otherwise known as Collaborative Tagging Systems, enable Internet users to share, annotate and search for online resources with user selected labels called tags. Tag recommendation, the suggestion of an ordered set of tags during the annotation process, reduces the user effort from a keyboard entry to a mouse click. By simplifying the annotation process tagging is promoted, noise in the data is reduced through the elimination of discrepancies that result in redundant tags, and ambiguous tags may be avoided. Tag recommenders can suggest tags that maximize utility, offer tags the user may not have previously considered or steer users toward adopting a core vocabulary. In sum, tag recommendation promotes a denser dataset that is useful in its own right or can be exploited by a myriad of data mining techniques for additional functionality. While there exists a long history of recommendation algorithms, the data structure of a Folksonomy is distinct from those found in traditional recommendation problems. We first explore two data reduction techniques, p-core processing and Hebbian deflation, then demonstrate how to adaptK-Nearest Neighbor for use with Folksonomies by incorporating user, resource and tag information into the algorithm. We further investigate multiple techniques for user modeling required to compute the similarity among users. Additionally we demonstrate that tag boosting, the promoting of tags previously applied by a user to a resource, improves the coverage and accuracy of K-Nearest Neighbor. These techniques are evaluated through extensive experimentation using data collected from two real Collaborative Tagging Web sites. Finally the modified K-Nearest Neighbor algorithm is compared with alternative techniques based on popularity and link analysis. We find that K-Nearest Neighbor modified for use with Folksonomies generates excellent recommendations, scales well with large datasets, and is applicable to both narrow and broadly focused Folksonomies. Copyright © 2009, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.},
  affiliation   = {Center for Web Intelligence School of Computing, DePaul University, Chicago, IL, United States},
  document_type = {Conference Paper},
  journal       = {CEUR Workshop Proceedings},
  source        = {Scopus},
  url           = {http://ceur-ws.org/Vol-528/paper8.pdf},
}

@Conference{martinez_etal_2009,
  author        = {Martínez, E. and Celma, O. and Sordo, M. and de Jong, B. and Serra, X.},
  title         = {Extending the folksonomies of freesound.org using content-based audio analysis},
  year          = {2009},
  pages         = {65-70},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {This paper presents an in-depth study of the social tagging mechanisms used in Freesound.org, an online community where users share and browse audio files by means of tags and content-based audio similarity search. We performed two analyses of the sound collection. The first one is related with how the users tag the sounds, and we could detect some well-known problems that occur in collaborative tagging systems (i.e. polysemy, synonymy, and the scarcity of the existing annotations). Moreover, we show that more than 10% of the collection were scarcely annotated with only one or two tags per sound, thus frustrating the retrieval task. In this sense, the second analysis focuses on enhancing the semantic annotations of these sounds, by means of content- based audio similarity (autotagging). In order to "autotag" the sounds, we use a k-NN classifier that selects the available tags from the most similar sounds. Human assessment is performed in order to evaluate the perceived quality of the candidate tags. The results show that, in 77% of the sounds used, the annotations have been correctly extended with the proposed tags derived from audio similarity.},
  affiliation   = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain},
  document_type = {Conference Paper},
  journal       = {Proceedings of the 6th Sound and Music Computing Conference, SMC 2009},
  source        = {Scopus},
  url           = {http://mtg.upf.edu/files/publications/SMC09_emartinez_ocelma_msordo_bdejong_xserra.pdf},
}

@InProceedings{chandramouli_etal_2009,
  author        = {K. Chandramouli and T. Kliegr and V. Svatek and E. Izquierdo},
  title         = {Towards semantic tagging in collaborative environments},
  booktitle     = {2009 16th International Conference on Digital Signal Processing},
  year          = {2009},
  pages         = {1-6},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Tags pose an efficient and effective way of organization of resources, but they are not always available. A technique called SCM/THD investigated in this paper extracts entities from free-text annotations, and using the Lin similarity measure over the WordNet thesaurus classifies them into a controlled vocabulary of tags. Hypernyms extracted from Wikipedia are used to map uncommon entities to Wordnet synsets. In collaborative environments, users can assign multiple annotations to the same object hence increasing the amount of information available. Assuming that the semantics of the annotations overlap, this redundancy can be exploited to generate higher quality tags. A preliminary experiment presented in the paper evaluates the consistency and quality of tags generated from multiple annotations of the same image. The results obtained on an experimental dataset comprising of 62 annotations from four annotators show that the accuracy of a simple majority vote surpasses the average accuracy obtained through assessing the annotations individually by 18%. A moderate-strength correlation has been found between the quality of generated tags and the consistency of annotations.},
  doi           = {10.1109/ICDSP.2009.5201138},
  issn          = {1546-1874},
  keywords      = {Web sites;text analysis;Lin similarity measure;SCM;THD;Wikipedia;WordNet thesaurus;free-text annotations;hypernyms;semantic tagging;Collaboration;Collaborative work;Computer science;Informatics;Knowledge engineering;Ontologies;Statistics;Tagging;Taxonomy;Wikipedia;Collaborative image tagging;Semantic Concept Mapping;Targeted Hypernym Discovery;User-Generated Content, rank2},
}

@InProceedings{shen_etal_2009,
  author        = {C. Shen and J. Jiao and Y. Yang and B. Wang},
  title         = {Multi-instance multi-label learning for automatic tag recommendation},
  booktitle     = {2009 IEEE International Conference on Systems, Man and Cybernetics},
  year          = {2009},
  pages         = {4910-4914},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {Tag services have recently become one of the most popular Internet services on the World Wide Web. Due to the fact that a Web page can be associate with multiple tags, previous research on tag recommendation mainly focuses on improving its accuracy or efficiency through multi-label learning algorithms. However, as a Web page can also be split into multiple sections and be represented as a bag of instances, multi-instance multi-label learning framework should fit this problem better. In this paper, we improve the performance of tag suggestion by using multi-instance multi-label learning. Each Web page is divided into a bag of instances. The experiments on real-word data from delicious suggest that our framework has better performance than traditional multi-label learning methods on the task of tag recommendation.},
  doi           = {10.1109/ICSMC.2009.5346261},
  issn          = {1062-922X},
  keywords      = {Internet;Web services;information filtering;learning (artificial intelligence);Internet services;Web page;World Wide Web;automatic tag recommendation;multiinstance multilabel learning algorithm;Computer science;Cybernetics;Learning systems;Machine learning;Machine learning algorithms;Tagging;USA Councils;Web and internet services;Web pages;Web sites;machine learning;multi-instance;multi-label;tag recommendation, rank1},
}

@InProceedings{li_etal_2009a,
  author        = {Xirong Li and C. G. M. Snoek and M. Worring},
  title         = {Annotating images by harnessing worldwide user-tagged photos},
  booktitle     = {2009 IEEE International Conference on Acoustics, Speech and Signal Processing},
  year          = {2009},
  pages         = {3717-3720},
  month         = {April},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic image tagging is important yet challenging due to the semantic gap and the lack of learning examples to model a tag's visual diversity. Meanwhile, social user tagging is creating rich multimedia content on the Web. In this paper, we propose to combine the two tagging approaches in a search-based framework. For an unlabeled image, we first retrieve its visual neighbors from a large user-tagged image database. We then select relevant tags from the result images to annotate the unlabeled image. To tackle the unreliability and sparsity of user tagging, we introduce a joint-modality tag relevance estimation method which efficiently addresses both textual and visual clues. Experiments on 1.5 million Flickr photos and 10 000 Corel images verify the proposed method.},
  doi           = {10.1109/ICASSP.2009.4960434},
  issn          = {1520-6149},
  keywords      = {image retrieval;relevance feedback;automatic image tagging;image annotation;image retrieval;joint-modality tag relevance estimation method;multimedia Web content;search-based framework;social user tagging;user-tagged image database;worldwide user-tagged photo;Cultural differences;Image databases;Image retrieval;Informatics;Information retrieval;Multimedia databases;Multimedia systems;Tagging;Video sharing;Visual databases;Automatic image tagging;User tagging},
}

@InProceedings{gallagher_etal_2009,
  author        = {A. Gallagher and D. Joshi and J. Yu and J. Luo},
  title         = {Geo-location inference from image content and user tags},
  booktitle     = {2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops},
  year          = {2009},
  pages         = {55-62},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Associating image content with their geographic locations has been increasingly pursued in the computer vision community in recent years. In a recent work, large collections of geotagged images were found to be helpful in estimating geo-locations of query images by simple visual nearest-neighbors search. In this paper, we leverage user tags along with image content to infer the geo-location. Our model builds upon the fact that the visual content and user tags of pictures can provide significant hints about their geo-locations. Using a large collection of over a million geotagged photographs, we build location probability maps of user tags over the entire globe. These maps reflect the picture-taking and tagging behaviors of thousands of users from all over the world, and reveal interesting tag map patterns. Visual content matching is performed using multiple feature descriptors including tiny images, color histograms, GIST features, and bags of textons. The combination of visual content matching and local tag probability maps forms a strong geo-inference engine. Large-scale experiments have shown significant improvements over pure visual content-based geo-location inference.},
  doi           = {10.1109/CVPRW.2009.5204168},
  issn          = {2160-7508},
  keywords      = {geography;image matching;query processing;visual databases;GIST features;color histograms;geographic location;geoinference engine;geolocation inference;geotagged images;geotagged photographs;image content;local tag probability map;location probability maps;multiple feature descriptor;query images;user tags;visual content matching;visual nearest neighbor search;Computer vision;Global Positioning System;Histograms;Humans;Image databases;Laboratories;Large-scale systems;Nearest neighbor searches;Search engines;Tagging},
}

@InProceedings{vinciarelli_etal_2009,
  author        = {A. Vinciarelli and N. Suditu and M. Pantic},
  title         = {Implicit Human-Centered Tagging},
  booktitle     = {2009 IEEE International Conference on Multimedia and Expo},
  year          = {2009},
  pages         = {1428-1431},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {This paper provides a general introduction to the concept of implicit human-centered tagging (IHCT) - the automatic extraction of tags from nonverbal behavioral feedback of media users. The main idea behind IHCT is that nonverbal behaviors displayed when interacting with multimedia data (e.g., facial expressions, head nods, etc.) provide information useful for improving the tag sets associated with the data. As such behaviors are displayed naturally and spontaneously, no effort is required from the users, and this is why the resulting tagging process is said to be "implicit". Tags obtained through IHCT are expected to be more robust than tags associated with the data explicitly, at least in terms of: generality (they make sense to everybody) and statistical reliability (all tags will be sufficiently represented). The paper discusses these issues in detail and provides an overview of pioneering efforts in the field.},
  doi           = {10.1109/ICME.2009.5202770},
  issn          = {1945-7871},
  keywords      = {human computer interaction;multimedia systems;implicit human-centered tagging;media users;multimedia data;nonverbal behavioral feedback;statistical reliability;tag automatic extraction;tagging process;Collaboration;Computer networks;Data mining;Educational institutions;Feedback;Indexing;Information retrieval;Robustness;Social network services;Tagging;Implicit Tagging;Nonverbal Behavior Analysis},
}

@InProceedings{chow_etal_2009,
  author        = {K. O. Chow and K. Y. K. Fan and A. Y. K. Chan and G. T. L. Wong},
  title         = {Content-Based Tag Generation for the Grouping of Tags},
  booktitle     = {2009 International Conference on Mobile, Hybrid, and On-line Learning},
  year          = {2009},
  pages         = {7-12},
  month         = {Feb},
  __markedentry = {[felipe:1]},
  abstract      = {A tagging system can encounter too few or too many tags. To solve these problems, we propose a content-based automatic generation of tags. Applied to an e-Learning 2.0 application, the proposal creates tags based on lecture slide contents, generating an adequate number of tags so as to allow the tagging system to start up effectively. It also lets student users to group tags. The tag grouping by relations yields more meaningful tag retrieval in the many tags case. This tag-based indexing of lecture slides provides better learning experience to students in studying lecture slides.},
  doi           = {10.1109/eLmL.2009.22},
  keywords      = {Internet;computer aided instruction;information retrieval;Web 2.0;content-based automatic tag generation;e-Learning 2.0 application;lecture slide contents;tag retrieval;tagging system;Application software;Blogs;Computer science;Electronic learning;Hybrid power systems;Internet;Proposals;Software tools;Tag clouds;Tagging;Tagging;Web 2.0;eLearning 2.0},
}

@InProceedings{liu_etal_2009a,
  author        = {Y. Liu and M. Liu and X. Chen and L. Xiang and Q. Yang},
  title         = {Automatic Tag Recommendation for Weblogs},
  booktitle     = {2009 International Conference on Information Technology and Computer Science},
  year          = {2009},
  volume        = {1},
  pages         = {546-549},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {There have been many researches on how to recommend tags for weblogs. In this paper, we propose a novel automatic tag recommendation algorithm, which can be used in the large-scale and real-time data process effectively and efficiently. Most existing researches on tag suggestion focus on firstly mining the relationship between testing and training data and then assigning the top ranked tags of the most related training data to the testing object. However, they ignore the internal relationship between tags and weblogs. According to our research, more than 43% tags, which have been labeled by weblog users, have actually been used in the body of the text. At the meanwhile, the term frequency distribution, the paragraph frequency distribution and the first occurrence position of tags are very different from the ones of non-tags in the text. In this paper, the tags of a weblog are assigned in two steps. First of all, some probability distributions of the word attributes are trained by the labeled training weblogs, and some keywords of a testing weblog are extracted as one part of the tags based on the probability distributions. Then the other part of the tags are obtained from the first part ones with the help of Latent Semantic Indexing (LSI) model. Experiments on a large-scale tagging dataset of weblogs 12 show that the average tagging time for a new weblog is less than 0.02 seconds, and over 74% testing weblogs are correctly labeled with the top 15 tags.},
  doi           = {10.1109/ITCS.2009.263},
  keywords      = {Web sites;data mining;identification technology;indexing;Weblogs;automatic tag recommendation;data mining;large-scale data process;latent semantic indexing;real-time data process;Frequency;Indexing;Internet;Large scale integration;Large-scale systems;Probability distribution;Tagging;Testing;Training data;Web pages;data mining;recommendation system},
}

@InProceedings{nikolopoulos_etal_2009,
  author        = {S. Nikolopoulos and E. Chatzilari and E. Giannakidou and I. Kompatsiaris},
  title         = {Towards fully un-supervised methods for generating object detection classifiers using social data},
  booktitle     = {2009 10th Workshop on Image Analysis for Multimedia Interactive Services},
  year          = {2009},
  pages         = {230-233},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {In this work a framework for constructing object detection classifiers using weakly annotated social data is proposed. Social information is combined with computer vision techniques to automatically obtain a set of images annotated at region-detail. All assumptions made to automate the proposed framework are driven by the reasonable expectation that due to the collaborative aspect of social data, linguistic descriptions and visual representations will start to converge on common concepts, as the scale of the analyzed dataset increases. Comparison tests performed against manually trained object detectors showed that comparable performance can be achieved.},
  doi           = {10.1109/WIAMIS.2009.5031475},
  issn          = {2158-5873},
  keywords      = {computer vision;object detection;computer vision techniques;fully unsupervised methods;linguistic descriptions;object detection classifiers;visual representations;Computer vision;Data analysis;Detectors;Feature extraction;Image generation;Image segmentation;Informatics;Object detection;Object recognition;Telematics, rank1},
}

@Conference{jaeschke_etal_2009,
  author          = {Jäschke, R. and Eisterlehner, F. and Hotho, A. and Stumme, G.},
  title           = {Testing and evaluating tag recommenders in a live system},
  year            = {2009},
  pages           = {369-372},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The challenge to provide tag recommendations for collaborative tagging systems has attracted quite some attention of researchers lately. However, most research focused on the evaluation and development of appropriate methods rather than tackling the practical challenges of how to integrate recommendation methods into real tagging systems, record and evaluate their performance. In this paper we describe the tag recommendation framework we developed for our social bookmark and publication sharing system BibSonomy. With the intention to develop, test, and evaluate recommendation algorithms and supporting cooperation with researchers, we designed the framework to be easily extensible, open for a variety of methods, and usable independent from BibSonomy. Furthermore, this paper presents a first evaluation of two exemplarily deployed recommendation methods. Copyright 2009 ACM.},
  affiliation     = {Knowledge and Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author_keywords = {Framework; Social bookmarking; Tag recommender},
  document_type   = {Conference Paper},
  doi             = {10.1145/1639714.1639790},
  journal         = {RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1639714.1639790},
}

@Conference{sharma_bedi_2009,
  author          = {Sharma, R. and Bedi, P.},
  title           = {Personalized tag recommendations to enhance user's perception},
  booktitle       = {2009 International Conference on Advances in Recent Technologies in Communication and Computing},
  year            = {2009},
  pages           = {944-947},
  month           = {Oct},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Tagging is a process whereby users freely choose keywords to label web objects in order to share or recover them later. Tags associated to an object by the user depict his viewpoint or perception. The perception of the target user can be enhanced by aggregating and analyzing the tags associated to an object by other users who share at least one tag with the target user in common for that object and recommending tags to him. Most of the existing techniques recommend tags on the basis of their popularity among the users. The proposed approach complement these approaches by taking the temporal nature of interests of the user into account and enhancing his perception by analyzing and finding relationship among the tags associated to an object by him and other users. This relationship is analyzed using pheromone updating strategy known from ant algorithms for computing the weights on the edges of the co-occurrence graph containing tags as nodes. To observe the performance of our approach, experiments are carried out on the data collected from del.icio.us, a social book marking site that allows the users to tag URLs and share them with other people. © 2009 IEEE.},
  affiliation     = {Department of Computer Science, University of Delhi, Delhi, 110007, India},
  art_number      = {5328650},
  author_keywords = {Ant colony; Collaborative tagging systems; del.icio.us; Pheromone updating; Swarm intelligence},
  document_type   = {Conference Paper},
  doi             = {10.1109/ARTCom.2009.96},
  journal         = {ARTCom 2009 - International Conference on Advances in Recent Technologies in Communication and Computing},
  keywords        = {Internet;information filtering;Web object label;ant algorithm;cooccurrence graph;personalized tag recommendation;pheromone updating strategy;user perception;Algorithm design and analysis;Books;Collaboration;Communications technology;Computer science;Iterative algorithms;Organizing;Particle swarm optimization;Tagging;Uniform resource locators;Ant colony;Collaborative tagging systems;Swarm Intelligence;del.icio.us;pheromone updating, rank1},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/5328650/},
}

@Article{hsieh_etal_2009,
  author          = {Hsieh, W.-T. and Stu, J. and Chen, Y.-L. and Chou, S.-C.T.},
  title           = {A collaborative desktop tagging system for group knowledge management based on concept space},
  journal         = {Expert Systems with Applications},
  year            = {2009},
  volume          = {36},
  number          = {5},
  pages           = {9513-9523},
  issn            = {0957-4174},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The advent of internet has led to a significant growth in the amount of information available, resulting in information overload, i.e. individuals have too much information to make a decision. To resolve this problem, collaborative tagging systems form a categorization called folksonomy in order to organize web resources. A folksonomy aggregates the results of personal free tagging of information and objects to form a categorization structure that applies utilizes the collective intelligence of crowds. Folksonomy is more appropriate for organizing huge amounts of information on the Web than traditional taxonomies established by expert cataloguers. However, the attributes of collaborative tagging systems and their folksonomy make them impractical for organizing resources in personal environments. This work designs a desktop collaborative tagging (DCT) system that enables collaborative workers to tag their documents. This work proposes an application in patent analysis based on the DCT system. Folksonomy in DCT is built by aggregating personal tagging results, and is represented by a concept space. Concept spaces provide synonym control, tag recommendation and relevant search. Additionally, to protect privacy of authors and to decrease the transmission cost, relations between tagged and untagged documents are constructed by extracting document's features rather than adopting the full text. Experimental results reveal that the adoption rate of recommended tags for new documents increases by 10% after users have tagged five or six documents. Furthermore, DCT can recommend tags with higher adoption rates when given new documents with similar topics to previously tagged ones. The relevant search in DCT is observed to be superior to keyword search when adopting frequently used tags as queries. The average precision, recall, and F-measure of DCT are 12.12%, 23.08%, and 26.92% higher than those of keyword searching. DCT allows a multi-faceted categorization of resources for collaborative workers and recommends tags for categorizing resources to simplify categorization easier. Additionally, DCT system provides relevance searching, which is more effective than traditional keyword searching for searching personal resources. Crown Copyright © 2008.},
  affiliation     = {Department of Information Management, National Taiwan University, Taipei, Taiwan; Institute for Information Industry, Taipei, Taiwan},
  author_keywords = {Collaborative tagging; Concept space; Folksonomy; Personal information management; Tagging},
  document_type   = {Article},
  doi             = {10.1016/j.eswa.2008.12.042},
  keywords        = {Tagging, Collaborative tagging, Folksonomy, Concept space, Personal information management},
  source          = {Scopus},
  url             = {http://linkinghub.elsevier.com/retrieve/pii/S0957417408008853},
}

@Conference{bundschus_etal_2009,
  author          = {Bundschus, M. and Yu, S. and Tresp, V. and Rettinger, A. and Dejori, M. and Kriegel, H.-P.},
  title           = {Hierarchical bayesian models for collaborative tagging systems},
  booktitle       = {2009 Ninth IEEE International Conference on Data Mining},
  year            = {2009},
  pages           = {728-733},
  month           = {Dec},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems with user generated content have become a fundamental element of websites such as Delicious, Flickr or CiteULike. By sharing common knowledge, massively linked semantic data sets are generated that provide new challenges for data mining. In this paper, we reduce the data complexity in these systems by finding meaningful topics that serve to group similar users and serve to recommend tags or resources to users. We propose a well-founded probabilistic approach that can model every aspect of a collaborative tagging system. By integrating both user information and tag information into the well-known Latent Dirichlet Allocation framework, the developed models can be used to solve a number of important information extraction and retrieval tasks. © 2009 IEEE.},
  affiliation     = {Institute for Computer Science, Ludwig-Maximilians-Universität München, Oettingenstr. 67, 80538 München, Germany; CAD and Knowledge Solutions, Siemens Medical Solutions, 51 Valley Stream Parkway, Malvern, PA 19355, United States; Corporate Technology, Siemens AG, Otto-Hahn-Ring 6, 81739 München, Germany; Institute for Computer Science (i7), Technische Universität München, Boltzmannstr. 3, 85748 Garching, Germany; Siemens Corporate Research, Integrated Data Systems Dep., 755 College Road East, Princeton, NJ 08540, United States},
  art_number      = {5360302},
  author_keywords = {Collaborative tagging; LDA; User modeling},
  document_type   = {Conference Paper},
  doi             = {10.1109/ICDM.2009.121},
  issn            = {1550-4786},
  journal         = {Proceedings - IEEE International Conference on Data Mining, ICDM},
  keywords        = {Bayes methods;Web sites;data mining;groupware;identification technology;Web sites;collaborative tagging systems;data mining;hierarchical Bayesian models;latent Dirichlet allocation framework;Bayesian methods;Computer science;Data mining;Data systems;Educational institutions;Information retrieval;International collaboration;Linear discriminant analysis;Tagging;USA Councils;LDA;collaborative tagging;user modeling, rank2},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/5360302/},
}

@Conference{kamishima_etal_2009,
  author          = {Kamishima, T. and Hamasaki, M. and Akaho, S.},
  title           = {TrBagg: A simple transfer learning method and its application to personalization in collaborative tagging},
  booktitle       = {2009 Ninth IEEE International Conference on Data Mining},
  year            = {2009},
  pages           = {219-228},
  month           = {Dec},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The aim of transfer learning is to improve prediction accuracy on a target task by exploiting the training examples for tasks that are related to the target one. Transfer learning has received more attention in recent years, because this technique is considered to be helpful in reducing the cost of labeling. In this paper, we propose a very simple approach to transfer learning: TrBagg, which is the extension of bagging. TrBagg is composed of two stages: Many weak classifiers are first generated as in standard bagging, and these classifiers are then filtered based on their usefulness for the target task. This simplicity makes it easy to work reasonably well without severe tuning of learning parameters. Further, our algorithm equips an algorithmic scheme to avoid negative transfer. We applied TrBagg to personalized tag prediction tasks for social bookmarks Our approach has several convenient characteristics for this task such as adaptation to multiple tasks with low computational cost. © 2009 IEEE.},
  affiliation     = {National Institute of Advanced Industrial Science and Technology (AIST), AIST Tsukuba Central 2, Umezono 1-1-1, Tsukuba, Ibaraki, 305-8568, Japan},
  art_number      = {5360247},
  author_keywords = {Bagging; Collaborative tagging; Ensemble learning; Personalization; Transfer learning},
  document_type   = {Conference Paper},
  doi             = {10.1109/ICDM.2009.9},
  issn            = {1550-4786},
  journal         = {Proceedings - IEEE International Conference on Data Mining, ICDM},
  keywords        = {information filtering;learning (artificial intelligence);TrBagg;collaborative tagging;multilabel classification problems;personalized tag prediction tasks;simple transfer learning method;social bookmarks;Bagging;Collaboration;Computational efficiency;Costs;Filtering;Industrial training;Labeling;Learning systems;Machine learning algorithms;Tagging;bagging;collaborative tagging;ensemble learning;personalization;recommender system;transfer learning},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/5360247/},
}

@InProceedings{trattner_etal_2010,
  author        = {C. Trattner and D. Helic and S. Maglajlic},
  title         = {Enriching tagging systems with Google query tags},
  booktitle     = {Proceedings of the ITI 2010, 32nd International Conference on Information Technology Interfaces},
  year          = {2010},
  pages         = {205-210},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {As recent research shows, efficient navigability of tagging systems is only possible if the number of tags grows hand in hand with the number of tagged resources. However, the number of resources grows typically faster than the number of tags. In this paper we analyze how enriching of user tags with tags generated from Google queries influences navigability in tagging systems. The analysis dataset comes from an online encyclopedia called Austria-Forum. The first results are promising and show an increase in the number of resources that can be efficiently reached by navigation.},
  issn          = {1330-1012},
  keywords      = {query processing;social networking (online);user interfaces;Austria-Forum encyclopedia;Google query tags;tagging systems;user tags;Data analysis;Encyclopedias;Google;Navigation;Tag clouds;Vocabulary;Tagging systems;navigation;resources;social networks;tag clouds;tags},
  url           = {http://ieeexplore.ieee.org/document/5546393/},
}

@InProceedings{hao_zhong_2010,
  author        = {F. Hao and S. Zhong},
  title         = {ECKDF: Extended conceptual knowledge discovery in folksonomy},
  booktitle     = {International Conference on Computational Problem-Solving},
  year          = {2010},
  pages         = {71-76},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Social bookmarking tools are rapidly emerging on the Web. A conceptual structure called folksonomy plays an important role in such systems. The folksonomy is constitute of tagging data(users, tags, resources) which organizing and classifying information on the Web. Tagging data stored in the folksonomy includes a lot of very useful information and knowledge. Unlike ontologies, shared conceptualizations in folksonomy are not formalized and it is rather implicit. The hidden knowledge Discovering from folksonomy is becoming the main research task among the social sharing resources systems. In this paper, we propose an approach of folksonomy data mining based on Variable Precise Concepts (VPC) for discovering the extended conceptual knowledge(tag recommendation, resources suggestion) from folksonomy. Finally, the feasibility and efficiency of our approach are demonstrated by experiments.},
  keywords      = {data mining;social networking (online);ECKDF;extended conceptual knowledge discovery;folksonomy data mining;hidden knowledge Discovering;information classifying;ontologies;shared conceptualizations;social bookmarking tools;social sharing resources systems;tagging;variable precise concepts;Artificial intelligence;Artificial neural networks;Context;Data mining;Delta modulation;Lattices;Tagging},
  url           = {http://ieeexplore.ieee.org/document/5696014/},
}

@Article{symeonidis_etal_2010,
  author        = {P. Symeonidis and A. Nanopoulos and Y. Manolopoulos},
  title         = {A Unified Framework for Providing Recommendations in Social Tagging Systems Based on Ternary Semantic Analysis},
  journal       = {IEEE Transactions on Knowledge and Data Engineering},
  year          = {2010},
  volume        = {22},
  number        = {2},
  pages         = {179-192},
  month         = {Feb},
  issn          = {1041-4347},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is the process by which many users add metadata in the form of keywords, to annotate and categorize items (songs, pictures, Web links, products, etc.). Social tagging systems (STSs) can provide three different types of recommendations: They can recommend 1) tags to users, based on what tags other users have used for the same items, 2) items to users, based on tags they have in common with other similar users, and 3) users with common social interest, based on common tags on similar items. However, users may have different interests for an item, and items may have multiple facets. In contrast to the current recommendation algorithms, our approach develops a unified framework to model the three types of entities that exist in a social tagging system: users, items, and tags. These data are modeled by a 3-order tensor, on which multiway latent semantic analysis and dimensionality reduction is performed using both the higher order singular value decomposition (HOSVD) method and the kernel-SVD smoothing technique. We perform experimental comparison of the proposed method against state-of-the-art recommendation algorithms with two real data sets (Last.fm and BibSonomy). Our results show significant improvements in terms of effectiveness measured through recall/precision.},
  doi           = {10.1109/TKDE.2009.85},
  keywords      = {data analysis;meta data;recommender systems;singular value decomposition;social networking (online);tensors;3-order tensor;dimensionality reduction;higher order singular value decomposition method;kernel-SVD smoothing technique;metadata;multiway latent semantic analysis;social tagging systems;state-of-the-art recommendation algorithms;ternary semantic analysis;HOSVD.;Social tags;recommender systems;tensors, rank1},
}

@Article{zhao_etal_2010,
  author        = {W. L. Zhao and X. Wu and C. W. Ngo},
  title         = {On the Annotation of Web Videos by Efficient Near-Duplicate Search},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2010},
  volume        = {12},
  number        = {5},
  pages         = {448-461},
  month         = {Aug},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {With the proliferation of Web 2.0 applications, user-supplied social tags are commonly available in social media as a means to bridge the semantic gap. On the other hand, the explosive expansion of social web makes an overwhelming number of web videos available, among which there exists a large number of near-duplicate videos. In this paper, we investigate techniques which allow effective annotation of web videos from a data-driven perspective. A novel classifier-free video annotation framework is proposed by first retrieving visual duplicates and then suggesting representative tags. The significance of this paper lies in the addressing of two timely issues for annotating query videos. First, we provide a novel solution for fast near-duplicate video retrieval. Second, based on the outcome of near-duplicate search, we explore the potential that the data-driven annotation could be successful when huge volume of tagged web videos is freely accessible online. Experiments on cross sources (annotating Google videos and Yahoo! videos using YouTube videos) and cross time periods (annotating YouTube videos using historical data) show the effectiveness and efficiency of the proposed classifier-free approach for web video tag annotation.},
  doi           = {10.1109/TMM.2010.2050651},
  keywords      = {Internet;pattern classification;video retrieval;Web 2.0;Web video tag annotation;classifier-free video annotation framework;data-driven annotation;near-duplicate video retrieval;near-duplicate video search;social Web;social media;Data-driven;near-duplicate video search;video annotation;web video},
}

@InProceedings{jin_etal_2010,
  author        = {Y. Jin and R. Li and Z. Lu and K. Wen and X. Gu},
  title         = {Topic-Sensitive Tag Ranking},
  booktitle     = {2010 20th International Conference on Pattern Recognition},
  year          = {2010},
  pages         = {629-632},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is an increasingly popular way to describe and classify documents on the web. However, the quality of the tags varies considerably since the tags are authored freely. How to rate the tags becomes an important issue. In this paper, we propose a topic-sensitive tag ranking (TSTR) approach to rate the tags on the web. We employ a generative probabilistic model to associate each tag with a distribution of topics. Then we construct a tag graph according to the co-tag relationships and perform a topic-level random walk over the graph to suggest a ranking score for each tag at different topics. Experimental results validate the effectiveness of the proposed tag ranking approach.},
  doi           = {10.1109/ICPR.2010.159},
  issn          = {1051-4651},
  keywords      = {Internet;graph theory;probability;cotag relationships;generative probabilistic model;social tagging;tag graph;tag ranking approach;topic-level random walk;topic-sensitive tag ranking;Computer architecture;Graphical user interfaces;Java;Service oriented architecture;Tagging;Unified modeling language;Web search, rank1},
}

@InProceedings{al-kofahi_etal_2010,
  author        = {J. M. Al-Kofahi and A. Tamrawi and Tung Thanh Nguyen and Hoan Anh Nguyen and T. N. Nguyen},
  title         = {Fuzzy set approach for automatic tagging in evolving software},
  booktitle     = {2010 IEEE International Conference on Software Maintenance},
  year          = {2010},
  pages         = {1-10},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Software tagging has been shown to be an efficient, lightweight social computing mechanism to improve different social and technical aspects of software development. Despite the importance of tags, there exists limited support for automatic tagging for software artifacts, especially during the evolutionary process of software development. We conducted an empirical study on IBM Jazz's repository and found that there are several missing tags in artifacts and more precise tags are desirable. This paper introduces a novel, accurate, automatic tagging recommendation tool that is able to take into account users' feedbacks on tags, and is very efficient in coping with software evolution. The core technique is an automatic tagging algorithm that is based on fuzzy set theory. Our empirical evaluation on the real-world IBM Jazz project shows the usefulness and accuracy of our approach and tool.},
  doi           = {10.1109/ICSM.2010.5609751},
  issn          = {1063-6773},
  keywords      = {fuzzy set theory;software prototyping;IBM Jazz project;IBM Jazz repository;automatic tagging recommendation tool;evolutionary process;evolving software;fuzzy set approach;fuzzy set theory;lightweight social computing;software artifacts;software development;software evolution;software tagging;Correlation;Documentation;Fuzzy set theory;Organizations;Programming;Software;Tagging},
}

@Article{han_etal_2010,
  author        = {Y. Han and F. Wu and Y. Zhuang and X. He},
  title         = {Multi-Label Transfer Learning With Sparse Representation},
  journal       = {IEEE Transactions on Circuits and Systems for Video Technology},
  year          = {2010},
  volume        = {20},
  number        = {8},
  pages         = {1110-1121},
  month         = {Aug},
  issn          = {1051-8215},
  __markedentry = {[felipe:1]},
  abstract      = {Due to the visually polysemous barrier, videos and images may be annotated by multiple tags. Discovering the correlations among different tags can significantly help predicting precise labels for videos and images. Many of recent studies toward multi-label learning construct a linear subspace embedding with encoded multi-label information, such that data points sharing many common labels tend to be close to each other in the embedded subspace. Motivated by the advances of compressive sensing research, a sparse representation that selects a compact subset to describe the input data can be more discriminative. In this paper, we propose a sparse multi-label learning method to circumvent the visually polysemous barrier of multiple tags. Our approach learns a multi-label encoded sparse linear embedding space from a related dataset, and maps the target data into the learned new representation space to achieve better annotation performance. Instead of using l1-norm penalty (lasso) to induce sparse representation, we propose to formulate the multi-label learning as a penalized least squares optimization problem with elastic-net penalty. By casting the video concept detection and image annotation tasks into a sparse multi-label transfer learning framework in this paper, ridge regression, lasso, elastic net, and the multi-label extended sparse discriminant analysis methods are, respectively, well explored and compared.},
  doi           = {10.1109/TCSVT.2010.2057015},
  keywords      = {image retrieval;learning (artificial intelligence);optimisation;sparse matrices;video signal processing;image annotation tasks;least squares optimization problem;linear subspace;multilabel transfer learning;sparse representation;video concept detection;visually polysemous barrier;Image annotation;multi-label learning;sparse representation;transfer learning;video concept detection, rank1},
}

@InProceedings{ravindran_etal_2010,
  author        = {P. P. Ravindran and A. Mishra and P. Kesavan and S. Mohanavalli},
  title         = {Randomized tag recommendation in social networks and classification of spam posts},
  booktitle     = {2010 IEEE International Workshop on: Business Applications of Social Network Analysis (BASNA)},
  year          = {2010},
  pages         = {1-6},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommendation is an integral part of any bookmarking application. With the growing popularity in Web 2.0 usage, recommending tags is of utmost importance in enabling a user to perform bookmarking easily. An issue that most recommendation systems do not consider is that users have a tendency to choose from tags that are suggested to them, which might bias the true popular rankings of tags. In this paper we consider the problem of tag recommendation for bookmarks based on user feedback. We propose an approach for automatic tag recommendation by using a suggestion set and discuss a randomized suggestion rule for learning the true popular tags. As our algorithm depends on the frequency of tag suggestions, the action of spammers and malicious users may result in skewed ranking of tags for a bookmark. Hence, there arises a need to identify malicious users and spam posts to reinforce the efficiency of our algorithm because spammers can easily mislead a system. We have proposed an approach for classifying spammers based on tagging history of users. Our approach basically estimates the probability of a user being a spammer by analyzing previous posts and tags. Our analysis on a dataset of a popular bookmarking site shows that the proposed method is effective in suggesting the true popular tags and identifying spammers.},
  doi           = {10.1109/BASNA.2010.5730294},
  keywords      = {Internet;pattern classification;recommender systems;social networking (online);Web 2.0 usage;bookmarking application;popular tags learning;randomized suggestion rule;randomized tag recommendation system;social networks;spam posts classification;suggestion set;tag suggestion frequency;user feedback;Accuracy;Algorithm design and analysis;Classification algorithms;History;Tagging;Unsolicited electronic mail;Spam Factor;Stagnation rate;bookmarking;popularity;recommendation;social networks;spam;tag},
}

@InProceedings{hassan_etal_2010,
  author        = {M. T. Hassan and A. Karim and F. Javed and N. Arshad},
  title         = {Self-Optimizing a Clustering-based Tag Recommender for Social Bookmarking Systems},
  booktitle     = {2010 Ninth International Conference on Machine Learning and Applications},
  year          = {2010},
  pages         = {601-606},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper, we propose and evaluate a self-optimization strategy for a clustering-based tag recommendation system. For tag recommendation, we use an efficient discriminative clustering approach. To develop our self-optimization strategy for this tag recommendation approach, we empirically investigate when and how to update the tag recommender with minimum human intervention. We present a nonlinear optimization model whose solution yields the clustering parameters that maximize the recommendation accuracy within an administrator specified time window. Evaluation on "BibSonomy'' data produces promising results. For example, by using our self-optimization strategy a 6% increase in average F1 score is achieved when the administrator allows up to 2% drop in average F1 score in the last one thousand recommendations.},
  doi           = {10.1109/ICMLA.2010.93},
  keywords      = {nonlinear programming;recommender systems;BibSonomy data;clustering-based tag recommendation system;clustering-based tag recommender;discriminative clustering;minimum human intervention;nonlinear optimization model;self-optimization strategy;social bookmarking systems;tag recommendation approach;Accuracy;Clustering methods;Optimization;Polynomials;Power capacitors;Tagging;Training;clustering;self-optimization;tag recommendation, rank1},
}

@InProceedings{rendle_2010,
  author        = {S. Rendle},
  title         = {Factorization Machines},
  booktitle     = {2010 IEEE International Conference on Data Mining},
  year          = {2010},
  pages         = {995-1000},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper, we introduce Factorization Machines (FM) which are a new model class that combines the advantages of Support Vector Machines (SVM) with factorization models. Like SVMs, FMs are a general predictor working with any real valued feature vector. In contrast to SVMs, FMs model all interactions between variables using factorized parameters. Thus they are able to estimate interactions even in problems with huge sparsity (like recommender systems) where SVMs fail. We show that the model equation of FMs can be calculated in linear time and thus FMs can be optimized directly. So unlike nonlinear SVMs, a transformation in the dual form is not necessary and the model parameters can be estimated directly without the need of any support vector in the solution. We show the relationship to SVMs and the advantages of FMs for parameter estimation in sparse settings. On the other hand there are many different factorization models like matrix factorization, parallel factor analysis or specialized models like SVD++, PITF or FPMC. The drawback of these models is that they are not applicable for general prediction tasks but work only with special input data. Furthermore their model equations and optimization algorithms are derived individually for each task. We show that FMs can mimic these models just by specifying the input data (i.e. the feature vectors). This makes FMs easily applicable even for users without expert knowledge in factorization models.},
  doi           = {10.1109/ICDM.2010.127},
  issn          = {1550-4786},
  keywords      = {data mining;matrix decomposition;support vector machines;SVM;factorization machine;feature vector;model parameters;parameter estimation;sparse data;support vector machine;tensor factorization;factorization machine;sparse data;support vector machine;tensor factorization, rank1},
}

@InProceedings{krestel_fankhauser_2010,
  author        = {R. Krestel and P. Fankhauser},
  title         = {Language Models and Topic Models for Personalizing Tag Recommendation},
  booktitle     = {2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2010},
  volume        = {1},
  pages         = {82-89},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences. In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.},
  doi           = {10.1109/WI-IAT.2010.29},
  keywords      = {Internet;data mining;information retrieval;probability;recommender systems;text analysis;Web;data mining;language models;latent Dirichlet allocation;probabilistic model;search technology;tag recommendation algorithms;tagging system;tagging systems;text-based information retrieval;topic models;Clustering;Data mining;Personalization, rank1},
}

@InProceedings{hu_etal_2010,
  author        = {M. Hu and E. P. Lim and J. Jiang},
  title         = {A Probabilistic Approach to Personalized Tag Recommendation},
  booktitle     = {2010 IEEE Second International Conference on Social Computing},
  year          = {2010},
  pages         = {33-40},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {In this work, we study the task of personalized tag recommendation in social tagging systems. To include candidate tags beyond the existing vocabularies of the query resource and of the query user, we examine recommendation methods that are based on personomy translation, and propose a probabilistic framework for adopting translations from similar users (neighbors). We propose to use distributional divergence to measure the similarity between users in the context of personomy translation, and examine two variations of such divergence (similarity) measures. We evaluate the proposed framework on a benchmark dataset collected from BibSonomy, and compare with two groups of baseline methods: (i) personomy translation methods based solely on the query user; and (ii) collaborative filtering. The experimental results show that our neighbor based translation methods outperform these baseline methods significantly. Moreover, we show that adopting translations from neighbors indeed helps including more relevant tags than that based solely on the query user.},
  doi           = {10.1109/SocialCom.2010.15},
  keywords      = {identification technology;recommender systems;benchmark dataset;collaborative filtering;distributional divergence;divergence measure;personalized tag recommendation;personomy translation;personomy translation method;probabilistic approach;query resource vocabulary;query user;recommendation method;social tagging system;Context;Equations;Measurement;Probabilistic logic;Tagging;Training;Vocabulary;personalization;tag recommendation, rank2},
  url           = {http://ieeexplore.ieee.org/document/5590886/},
}

@InProceedings{hao_zhong_2010a,
  author        = {Fei Hao and Shengtong Zhong},
  title         = {Tag recommendation based on user interest lattice matching},
  booktitle     = {2010 3rd International Conference on Computer Science and Information Technology},
  year          = {2010},
  volume        = {1},
  pages         = {276-280},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is becoming more and more popular in various Web 2.0 applications nowadays. It is important for many web-sites with tagging capabilities like “delicious” or “flickr”. These social tagging systems usually include tag recommendation mechanism which assist users in tagging process by suggesting relevant tags to them, where tag recommendation is the task of predicting a personalized list of tags for a user given an item. In this paper, we propose an approach for tag recommendation based on users' interest lattice matching (UILM). UILM constructs the users' interest lattice according to users' interest context extracted from tagging data. Lattice Matching is then proposed and applied to obtain the users that are similar to the current user. Finally, we show the feasibility and efficiency of our approach through experiments.},
  doi           = {10.1109/ICCSIT.2010.5564702},
  keywords      = {data mining;recommender systems;relevance feedback;social networking (online);Web 2.0 application;social tagging system;tag recommendation;tagging data;user interest lattice matching;website;Lattices;Social tagging;Tag recommendation;User interest lattice},
}

@Article{bischoff_etal_2010,
  author        = {Kerstin Bischoff and Claudiu S. Firan and Wolfgang Nejdl and Raluca Paiu},
  title         = {Bridging the gap between tagging and querying vocabularies: Analyses and applications for enhancing multimedia IR},
  journal       = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year          = {2010},
  volume        = {8},
  number        = {2},
  pages         = {97 - 109},
  issn          = {1570-8268},
  note          = {Bridging the Gap—Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0 The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.websem.2010.04.004},
  keywords      = {Web 2.0, Tag analysis, Web information retrieval, Knowledge discovery, Tag recommendation},
  url           = {http://www.sciencedirect.com/science/article/pii/S1570826810000284},
}

@InProceedings{yin_etal_2010,
  author        = {Yin, Dawei and Xue, Zhenzhen and Hong, Liangjie and Davison, Brian D.},
  title         = {A Probabilistic Model for Personalized Tag Prediction},
  booktitle     = {Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year          = {2010},
  series        = {KDD '10},
  pages         = {959--968},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1835925},
  doi           = {10.1145/1835804.1835925},
  isbn          = {978-1-4503-0055-1},
  keywords      = {personalized tag prediction, social tagging, tag prediction, rank2},
  location      = {Washington, DC, USA},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1835804.1835925},
}

@Article{shu_etal_2010,
  author          = {Shu, Z. and Yu, L. and Yang, X.},
  title           = {Personalized tag recommendation based on user preference and content},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2010},
  volume          = {6441 LNAI},
  number          = {PART 2},
  pages           = {348-355},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {With the widely use of collaborative tagging system nowadays, users could tag their favorite resources with free keywords. Tag recommendation technology is developed to help users in the process of tagging. However, most of the tag recommendation methods are merely based on the content of tagged resource. In this paper, it is argued that tags depend not only on the content of resource, but also on user preference. As such, a hybrid personalized tag recommendation method based on user preference and content is proposed. The experiment results show that the proposed method has advantages over traditional content-based methods. © 2010 Springer-Verlag.},
  affiliation     = {School of Information, Renmin University of China, Beijing 100872, China},
  author_keywords = {Collaborative Tagging; Tag Recommendation; Web2.0},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-17313-4_34},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-642-17313-4_34},
}

@Conference{lipczak_milios_2010,
  author          = {Lipczak, M. and Milios, E.},
  title           = {Learning in efficient tag recommendation},
  year            = {2010},
  pages           = {167-174},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The objective of a tag recommendation system is to propose a set of tags for a resource to ease the tagging process done manually by a user. Tag recommendation is an interesting and well defined research problem. However, while solving it, it is easy to forget about its practical implications. We discuss the practical aspects of tag recommendation and propose a system that successfully addresses the problem of learning in tag recommendation, without sacrificing ef-ficiency. Learning is realized in two aspects: adaptation to newly added posts and parameter tuning. The content of each added post is used to update the resource and user profiles as well as associations between tags. Parameter tuning allows the system to automatically adjust the way tag sources (e.g., content related tags or user profile tags) are combined to match the characteristics of a specific collaborative tagging system. The evaluation on data from three collaborative tagging systems conffrmed the importance of both learning methods. Finally, an architecture based on text indexing makes the system eficient enough to serve in real time collaborative tagging systems with number of posts counted in millions, given limited computing resources. Copyright 2010 ACM.},
  affiliation     = {Faculty of Computer Science, Dalhousie University, Halifax B3H 1W5, Canada},
  author_keywords = {Collaborative tagging; Folksonomies; Tag recommendation},
  document_type   = {Conference Paper},
  doi             = {10.1145/1864708.1864741},
  journal         = {RecSys'10 - Proceedings of the 4th ACM Conference on Recommender Systems},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78649955025&doi=10.1145%2f1864708.1864741&partnerID=40&md5=8959d5e5915a7da5047608a1ac326e56},
}

@Article{pudota_etal_2010,
  author        = {Pudota, N. and Dattolo, A. and Baruzzo, A. and Ferrara, F. and Tasso, C.},
  title         = {Automatic keyphrase extraction and ontology mining for content-based tag recommendation},
  journal       = {International Journal of Intelligent Systems},
  year          = {2010},
  volume        = {25},
  number        = {12},
  pages         = {1158-1186},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging represents for the Web a potential way for organizing and sharing information and for heightening the capabilities of existing search engines. However, because of the lack of automatic methodologies for generating the tags and supporting the tagging activity, many resources on the Web are deficient in tag information, and recommending opportune tags is both a current open issue and an exciting challenge. This paper approaches the problem by applying a combined set of techniques and tools (that uses tags, domain ontologies, keyphrase extraction methods) thereby generating tags automatically. The proposed approach is implemented in the PIRATES (Personalized Intelligent tag Recommender and Annotator TEStbed) framework, a prototype system for personalized content retrieval, annotation, and classification. A case study application is developed using a domain ontology for software engineering. © 2010 Wiley Periodicals, Inc.},
  affiliation   = {Artificial Intelligence Laboratory, Department of Mathematics and Computer Science, University of Udine, 33100 Udine, Italy},
  document_type = {Article},
  doi           = {10.1002/int.20448},
  keywords      = {rank2},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78349275289&doi=10.1002%2fint.20448&partnerID=40&md5=e8ed175082bdb98563da682456dd4d83},
}

@Conference{voon_etal_2010,
  author          = {Voon, W.P. and Mustapha, N. and Sulaiman, M.N.},
  title           = {A tag recommendation approach based on social semantic web},
  year            = {2010},
  pages           = {909-912},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging applications or folksonomies allow internet users to annotate resources with personalized tags. However, freedom afforded users come at a cost: an uncontrolled vocabulary can result in tag ambiguity hindering navigation. Ambiguity can mislead users as they search for relevant resources. Recommenders that avoid ambiguous tags may be penalized by standard utility metrics for not promoting such tags. To provide effectiveness of tag recommendation in term of accuracy and coverage, we propose a new method for tag recommendation approach based on social semantic web to overcome ambiguity and presenting users tags and resources that correspond more closely. This method is a combination of semantic content analysis methods and social network analysis methods. Copyright 2010 ACM.},
  affiliation     = {Faculty of Computer Science and Information Technology, University Putra Malaysia, 43400 Serdang, Selangor, Malaysia},
  author_keywords = {Ambiguity; Folksonomy; Recommender system; Semantic web; Social semantic web; Tag},
  document_type   = {Conference Paper},
  doi             = {10.1145/1967486.1967645},
  journal         = {iiWAS2010 - 12th International Conference on Information Integration and Web-Based Applications and Services},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1967486.1967645},
}

@Article{menezes_etal_2010,
  author        = {Menezes, G.V. and Almeida, J.M. and Belém, F. and Gonçalves, M.A. and Lacerda, A. and De Moura, E.S. and Pappa, G.L. and Veloso, A. and Ziviani, N.},
  title         = {Demand-driven tag recommendation},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2010},
  volume        = {6322 LNAI},
  number        = {PART 2},
  pages         = {402-417},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging allows users to assign arbitrary keywords (or tags) describing the content of objects, which facilitates navigation and improves searching without dependence on pre-configured categories. In large-scale tag-based systems, tag recommendation services can assist a user in the assignment of tags to objects and help consolidate the vocabulary of tags across users. A promising approach for tag recommendation is to exploit the co-occurrence of tags. However, these methods are challenged by the huge size of the tag vocabulary, either because (1) the computational complexity may increase exponentially with the number of tags or (2) the score associated with each tag may become distorted since different tags may operate in different scales and the scores are not directly comparable. In this paper we propose a novel method that recommends tags on a demand-driven basis according to an initial set of tags applied to an object. It reduces the space of possible solutions, so that its complexity increases polynomially with the size of the tag vocabulary. Further, the score of each tag is calibrated using an entropy minimization approach which corrects possible distortions and provides more precise recommendations. We conducted a systematic evaluation of the proposed method using three types of media: audio, bookmarks and video. The experimental results show that the proposed method is fast and boosts recommendation quality on different experimental scenarios. For instance, in the case of a popular audio site it provides improvements in precision (p@5) ranging from 6.4% to 46.7% (depending on the number of tags given as input), outperforming a recently proposed co-occurrence based tag recommendation method. © 2010 Springer-Verlag Berlin Heidelberg.},
  affiliation   = {Universidade Federal de Minas Gerais, Belo Horizonte, Brazil; Universidade Federal Do Amazonas, Manaus, Brazil},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-642-15883-4_26},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-78049410660&doi=10.1007%2f978-3-642-15883-4_26&partnerID=40&md5=c2ae19e102198d28f7b997efe583bf75},
}

@Article{jiang_etal_2010,
  author          = {Jiang, B. and Ling, Y. and Wang, J.},
  title           = {Tag recommendation based on social comment network},
  journal         = {International Journal of Digital Content Technology and its Applications},
  year            = {2010},
  volume          = {4},
  number          = {8},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Tagging has rapidly become a popular way to annotate the content on social network sites. Tags describe the contents of the resource or provide additional contextual and semantical information to make the content more easily browsable and discoverable by others. However, as tagging is not constrained by a controlled vocabulary and a certain number of resources have no comments, tags tend to be noisy and sparse. In this paper we show that a substantial level of local lexical and topical alignment is observable among users who lie close to each other in the social comment network. We analyze a representative snapshot of Flickr and construct comment networks that present the user clusters in which users add comments to the central user's photos. We find similar users with common interests based on K-Nearest Neighbor and present tag recommendation strategy in local comment context. The results of the evaluation show that tag recommendation based on local lexicon is an effective way to improve collaborative resource sharing in social network.},
  affiliation     = {School of Computer and Information Engineering, Zhejiang Gongshang University, Iran},
  author_keywords = {Collaborative tagging; Folksonomies; Social networks; Tag co-occurrence; Tag recommendation},
  document_type   = {Article},
  doi             = {10.4156/jdcta.vol4.issue8.12},
  source          = {Scopus},
  url             = {https://pdfs.semanticscholar.org/71bd/bc58b4fd19b5fafb145d320d0d08add03aa9.pdf},
}

@Conference{ang_etal_2010,
  author        = {Ang, H.H. and Gopalkrishnan, V. and Ng, W.K. and Hoi, S.C.H.},
  title         = {P2PDocTagger: Content management through automated P2P collaborative tagging},
  year          = {2010},
  volume        = {3},
  number        = {2},
  pages         = {1601-1604},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {As the amount of user generated content grows, personal information management has become a challenging problem. Several information management approaches, such as desktop search, document organization and (collaborative) document tagging have been proposed to address this, however they are either inappropriate or inefficient. Automated collaborative document tagging approaches mitigate the problems of manual tagging, but they are usually based on centralized settings which are plagued by problems such as scalability, privacy, etc. To resolve these issues, we present P2P DocTagger, an automated and distributed document tagging system based on classification in P2P networks. P2P-DocTagger minimizes the efforts of individual peers and reduces computation and communication cost while providing high tagging accuracy, and eases of document organization/retrieval. In addition, we provide a realistic and flexible simulation toolkit - P2PDMT, to facilitate the development and testing of P2P data mining algorithms. © 2010 VLDB Endowment.},
  affiliation   = {Nanyang Technological University, Singapore},
  document_type = {Article},
  journal       = {Proceedings of the VLDB Endowment},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84865570113&partnerID=40&md5=5848279909aa0487dcdf8c14ccd3ef7c},
}

@Article{yi_2010,
  author        = {Yi, K.},
  title         = {A semantic similarity approach to predicting library of congress subject headings for social tags},
  journal       = {Journal of the American Society for Information Science and Technology},
  year          = {2010},
  volume        = {61},
  number        = {8},
  pages         = {1658-1672},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging or collaborative tagging has become a new trend in the organization, management, and discovery of digital information. The rapid growth of shared information mostly controlled by social tags poses a new challenge for social tag-based information organization and retrieval. A plausible approach for this challenge is linking social tags to a controlled vocabulary. As an introductory step for this approach, this study investigates ways of predicting relevant subject headings for resources from social tags assigned to the resources. The prediction of subject headings was measured by five different similarity measures: tf-idf, cosine-based similarity (CoS), Jaccard similarity (or Jaccard coefficient; JS), Mutual information (MI), and information radius (IRad). Their results were compared to those by professionals. The results show that a CoS measure based on top five social tags was most effective. Inclusions of more social tags only aggravate the performance. The performance of JS is comparable to the performance of CoS while tf-idf is comparable with up to 70% less than the best performance. MI and IRad have inferior performance compared to the other methods. This study demonstrates the application of the similarity measuring techniques to the prediction of correct Library of Congress subject headings. © 2010 ASIS&T.},
  affiliation   = {School of Library and Information Science, University of Kentucky, 331 Little Library Building, Lexington, KY 40506-0224, United States},
  document_type = {Article},
  doi           = {10.1002/asi.21351},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954894187&doi=10.1002%2fasi.21351&partnerID=40&md5=91025d7a91b0a1b5f78be64d37c54188},
}

@Article{yap_etal_2010,
  author          = {Yap, K.-H. and Wu, K. and Zhu, C.},
  title           = {Knowledge propagation in collaborative tagging for image retrieval},
  journal         = {Journal of Signal Processing Systems},
  year            = {2010},
  volume          = {59},
  number          = {2},
  pages           = {163-175},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {An important issue in current collaborative framework for media tagging is that some images or videos may not be annotated properly or even not annotated at all. In view of this, this paper proposes a new knowledge propagation scheme to automatically propagate keywords from a subset of annotated images to the unannotated ones. The main idea is based on image content analysis and training of keyword classifiers. An evolutionary scheme is utilized to find the salient regions in the annotated images, and the importance of the other regions is estimated using one-class support vector machine (OCSVM). An ensemble of variable-length radial basis function (VLRBF)-based classifiers is trained based on the visual features of the annotated images. The trained classifiers are then used for knowledge propagation. Experimental results using 100 concept categories demonstrate the effectiveness of the proposed method. © 2008 Springer Science+Business Media, LLC.},
  affiliation     = {School of Electrical and Electronic Engineering, Nanyang Technological University, Nanyang Avenue, Singapore 639798, Singapore},
  author_keywords = {Collaborative tagging; Content analysis; Keyword classifier; Knowledge propagation},
  document_type   = {Article},
  doi             = {10.1007/s11265-008-0288-1},
  source          = {Scopus},
  url             = {https://link.springer.com/article/10.1007%2Fs11265-008-0288-1},
}

@Conference{musto_etal_2010,
  author          = {Musto, C. and Narducci, F. and De Gemmis, M. and Lops, P. and Semeraro, G.},
  title           = {An IR-based approach to tag recommendation},
  year            = {2010},
  volume          = {560},
  pages           = {65-69},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Thanks to the continuous growth of collaborative platforms like YouTube, Flickr and Delicious, we are recently witnessing to a rapid evolution of web dynamics towards a more 'social' vision, called Web 2.0. In this context collaborative tagging systems are rapidly emerging as one of the most promising tools. However, as tags are handled in a simply syntactical way, collaborative tagging systems suffer of typical Information Retrieval (IR) problems like polysemy and synonymy: so, in order to reduce the impact of these drawbacks and to aid at the same time the so-called tag convergence, systems that assist the user in the task of tagging are required. In this paper we present a system, called STaR, that implements an IR-based approach for tag recommendation. Our approach, mainly based on the exploitation of a state-of-the-art IR-model called BM25, relies on two assumptions: firstly, if two or more resources share some common patterns (e.g. the same features in the textual description), we can exploit this information supposing that they could be annotated with similar tags. Furthermore, since each user has a typical manner to label resources, a tag recommender might exploit this information to weigh more the tags she already used to annotate similar resources. We also present an experimental evaluation, carried out using a large dataset gathered from Bibsonomy. Copyright owned by the authors.},
  affiliation     = {Dept. of Computer Science, University of Bari Aldo Moro, Italy},
  author_keywords = {Collaborative tagging systems; Folksonomies; Recommender systems; Web 2.0},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84888266907&partnerID=40&md5=51f2eaf43e1263a22b76aa638d19fe89},
}

@Article{wang_etal_2010,
  author          = {Wang, J. and Clements, M. and Yang, J. and de Vries, A.P. and Reinders, M.J.T.},
  title           = {Personalization of tagging systems},
  journal         = {Information Processing and Management},
  year            = {2010},
  volume          = {46},
  number          = {1},
  pages           = {58-70},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Social media systems have encouraged end user participation in the Internet, for the purpose of storing and distributing Internet content, sharing opinions and maintaining relationships. Collaborative tagging allows users to annotate the resulting user-generated content, and enables effective retrieval of otherwise uncategorised data. However, compared to professional web content production, collaborative tagging systems face the challenge that end-users assign tags in an uncontrolled manner, resulting in unsystematic and inconsistent metadata. This paper introduces a framework for the personalization of social media systems. We pinpoint three tasks that would benefit from personalization: collaborative tagging, collaborative browsing and collaborative search. We propose a ranking model for each task that integrates the individual user's tagging history in the recommendation of tags and content, to align its suggestions to the individual user preferences. We demonstrate on two real data sets that for all three tasks, the personalized ranking should take into account both the user's own preference and the opinion of others. © 2009 Elsevier Ltd. All rights reserved.},
  affiliation     = {Department of Computer Science, University College London, United Kingdom; The Information and Communication Theory Group, Delft University of Technology, Netherlands; Loomia, United States; Centrum Wiskunde and Informatica, Netherlands},
  author_keywords = {Collaborative filtering; Collaborative tagging; Personalization; Social media; User-generated content},
  document_type   = {Article},
  doi             = {10.1016/j.ipm.2009.06.002},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349804696&doi=10.1016%2fj.ipm.2009.06.002&partnerID=40&md5=d4495842b294eb5cc416962492594433},
}

@Article{moxley_etal_2010,
  author        = {E. Moxley and T. Mei and B. S. Manjunath},
  title         = {Video Annotation Through Search and Graph Reinforcement Mining},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2010},
  volume        = {12},
  number        = {3},
  pages         = {184-193},
  month         = {April},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Unlimited vocabulary annotation of multimedia documents remains elusive despite progress solving the problem in the case of a small, fixed lexicon. Taking advantage of the repetitive nature of modern information and online media databases with independent annotation instances, we present an approach to automatically annotate multimedia documents that uses mining techniques to discover new annotations from similar documents and to filter existing incorrect annotations. The annotation set is not limited to words that have training data or for which models have been created. It is limited only by the words in the collective annotation vocabulary of all the database documents. A graph reinforcement method driven by a particular modality (e.g., visual) is used to determine the contribution of a similar document to the annotation target. The graph supplies possible annotations of a different modality (e.g., text) that can be mined for annotations of the target. Experiments are performed using videos crawled from YouTube. A customized precision-recall metric shows that the annotations obtained using the proposed method are superior to those originally existing for the document. These extended, filtered tags are also superior to a state-of-the-art semi-supervised technique for graph reinforcement learning on the initial user-supplied annotations.},
  doi           = {10.1109/TMM.2010.2041101},
  keywords      = {data mining;document handling;learning (artificial intelligence);video retrieval;annotations discovery;collective annotation vocabulary;customized precision recall metric;database documents;graph reinforcement mining;multimedia documents annotation;online media database;video annotation;Data mining;graph theory;video annotation;video content analysis},
}

@InProceedings{xiao_etal_2010,
  author        = {R. Xiao and Y. Ni and X. Du and P. Gong},
  title         = {Towards a Correlation Cooccurrence Model Generating Approach to Folksonomy},
  booktitle     = {2010 International Conference on Web Information Systems and Mining},
  year          = {2010},
  volume        = {2},
  pages         = {399-403},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {Folksonomy is a hyper graph data structure in social network, in which co occurrence is often used to act as an important means of recommender system. The co occurrence data information from folksonomy hyper graph is becoming increasingly important in recommending applications of social network. This paper presents an original method for easy random approach to generating correlation co occurrence model of folksonomy hyper graph in social network from a random user. The method creates classified co occurrence patterns, using resource items, tags, or users, correlation co occurrences obtained from two given corpora. Our experiments demonstrate that proposed approach can produces better stability for the recommender system. And our method offers a feasible means for developers to handle information co occurrence problems for folksonomy application.},
  doi           = {10.1109/WISM.2010.153},
  keywords      = {correlation theory;data structures;graph theory;pattern classification;random processes;recommender systems;social networking (online);cooccurrence data information;cooccurrence pattern classification;correlation cooccurrence model;folksonomy hypergraph;hypergraph data structure;information cooccurrence problems;random user;recommender system;social network;Correlation Cooccurrence;Folksonomy;Random Hypergraph partition},
}

@InProceedings{liu_fang_2010,
  author        = {K. Liu and B. Fang},
  title         = {Integrating Social Relations into Personalized Tag Recommendation},
  booktitle     = {2010 Second International Conference on Intelligent Human-Machine Systems and Cybernetics},
  year          = {2010},
  volume        = {1},
  pages         = {292-295},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Personalized tag recommendation is to provide a user with a ranked list of tags for a specific resource that best serves the user's needs. In this paper, we proposed a personalized tag recommendation algorithm incorporating with users' social relations. We model the social annotations made by the collaborative users and the social relations between them with a graph model. We associate each node in this graph with a tag preference vector, which is then refined through a random walk procedure over this graph. The tag preferences of the active user and resource are finally combined to generate the recommended tags. We conduct experiments on the Delicious. Experimental results demonstrate the effectiveness of the proposed algorithm.},
  doi           = {10.1109/IHMSC.2010.79},
  keywords      = {graph theory;recommender systems;social networking (online);user interfaces;collaborative user;graph model;personalized tag recommendation;random walk procedure;social annotation;social relation;tag preference vector;Approximation algorithms;Collaboration;Fans;Measurement;Social network services;Tagging;Training;personalization;social tagging;tag recommendation},
}

@InProceedings{si_sun_2010,
  author        = {X. Si and M. Sun},
  title         = {Tag Allocation Model: Model Noisy Social Annotations by Reason Finding},
  booktitle     = {2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2010},
  volume        = {1},
  pages         = {413-416},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {We propose the Tag Allocation Model (TAM) to model social annotation data. TAM is a probabilistic generative model, its key feature is finding the latent reason for each tag. A latent reason can be any discrete features of the document (such as words) or a global noise variable. Inferring the reason for each tag helps TAM reduce the ambiguity of a document with multiple tags. By introducing noise as a reason, TAM can handle noise tags naturally. We perform experiments on three real world data sets. The results show that TAM outperforms state-of-the-art approaches in both held-out perplexity and tag recommendation accuracy.},
  doi           = {10.1109/WI-IAT.2010.85},
  keywords      = {inference mechanisms;information retrieval;portals;probability;TAM;global noise variable;noisy social annotation data model;probabilistic generative model;real world data sets;reason finding;tag allocation model;probabilistic model;recommendation;social annotation;tagging, rank1},
}

@InProceedings{dostal_jezek_2010,
  author        = {M. Dostal and K. Ježek},
  title         = {Automatic tagging based on linked data: Unsupervised methods for the extraction of hidden information},
  booktitle     = {2010 IEEE International Conference on Service-Oriented Computing and Applications (SOCA)},
  year          = {2010},
  pages         = {1-4},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {We have created a web agent for collecting Call for Papers (CFP) announcements. Our web agent obtains CFP announcements from websites or from mailbox. The most important information is extracted and published on our own special website in a user and machine readable way. One of the most important problems is event classification, categorization and clustering. In this paper we describe unsupervised methods for automatic tagging based on information extraction from Linked data. These methods are usable in situations where we have to tag unknown data and we have no corpus for learning methods. Tagged data can have the form of short messages from RSS, short blog posts or emails. The automatic tags can be used for classifying the conferences. Users can use our web service to search for interesting events and sort them by their own preferences. We obtain tags with their relationship parameters and we can use them for automatic clustering of collected events.},
  doi           = {10.1109/SOCA.2010.5707152},
  issn          = {2163-2871},
  keywords      = {Web services;identification technology;semantic Web;automatic tagging;call for papers;categorization;clustering;event classification;information extraction;linked data;mailbox;semantic Web;web agent;web service;websites;Collaboration;Computer science;Data mining;Manuals;Tagging;Web services;Web sites;Automatic tagging;Linked Data;Semantic Web;Web 2.0},
}

@Conference{he_etal_2010,
  author          = {He, K. and He, L. and Lin, X. and Lu, W.},
  title           = {Social view based user modeling for recommendation in tagging systems by association rules},
  booktitle       = {2010 2nd International Workshop on Intelligent Systems and Applications},
  year            = {2010},
  pages           = {1-5},
  month           = {May},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Social tagging systems such as Facebook, YouTube, del.icio.us, Flickr become popular recent years and have achieved widespread success. State-of-art user modeling approaches in tagging systems usually use a vector of weighted tags. Unfortunately, typical user modeling methods using a vector of weighted tags which are based on personal view only and ignore the social view, have some inherent drawbacks. As in a social network like collaborative tagging system, it is subjective and incomplete to profile using only personal view. In this paper, a novel approach applying association rules is proposed to extend user profiles from the social view. The enriched user profile is a harvest from both personal view and social view. Algorithms of personalized recommendations for tags and items are presented. Also experimental results of using the profile we proposed are discussed. ©2010 IEEE.},
  affiliation     = {Department of Computer Science and Technology, East China Normal University, Shanghai, 200241, China; SMG Shanghai Interactive Television Co., Ltd., Shanghai, 200241, China},
  art_number      = {5473246},
  author_keywords = {Association rule; Model; Recommendation system; Social view; Tagging system},
  document_type   = {Conference Paper},
  doi             = {10.1109/IWISA.2010.5473246},
  journal         = {Proceedings - 2010 2nd International Workshop on Intelligent Systems and Applications, ISA 2010},
  keywords        = {data mining;groupware;identification technology;recommender systems;social networking (online);association rules;personalized recommendations algorithms;social network;social tagging systems;state of art user modeling approaches;user modeling;user profile;weighted tags vector;Association rules;Collaboration;Computer science;Facebook;Frequency;Helium;Social network services;TV;Tagging;YouTube},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/5473246/},
}

@Conference{rezel_liang_2010,
  author          = {Rezel, R. and Liang, S.},
  title           = {SWE-FE: Extending folksonomies to the Sensor Web},
  booktitle       = {2010 International Symposium on Collaborative Technologies and Systems},
  year            = {2010},
  pages           = {349-356},
  month           = {May},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {This paper presents SWE-FE: a suite of methods to extend folksonomies to the worldwide Sensor Web[1] in order to tackle the emergent data rich information poor (DRIP) syndrome afflicting most geospatial applications on the Internet. SWE-FE leverages the geospatial information associated with three key components of such collaborative tagging systems: tags, resources and users. Specifically, SWE-FE provides algorithms for: i) suggesting tags for users during the tag input stage; ii) generating tag maps which provides for serendipitous browsing; and iii) personalized searching within the folksonomy. We implement SWE-FE on the GeoCENS Sensor Web platform as a case study for assessing the efficacy of our methods. We outline the evaluation framework that we are currently employing to carry out this assessment. © 2010 IEEE.},
  affiliation     = {Department of Geomatics Engineering, Schulich School of Engineering, University of Calgary, 2500 University Drive NW, Calgary, AB T2N1N4, Canada},
  art_number      = {5478494},
  author_keywords = {Automated collaborative filtering; Folksonomy; Sensor web; Tag maps; Tags},
  document_type   = {Conference Paper},
  doi             = {10.1109/CTS.2010.5478494},
  journal         = {2010 International Symposium on Collaborative Technologies and Systems, CTS 2010},
  keywords        = {Internet;geographic information systems;wireless sensor networks;GeoCENS sensor Web platform;SWE-FE;data rich information poor syndrome;folksonomies;geospatial applications;worldwide sensor Web;Collaboration;Data engineering;Earth;Information filtering;Information filters;Internet;Monitoring;Tagging;Taxonomy;World Wide Web;Automated Collaborative Filtering;Folksonomy;Sensor Web;Tag Maps;Tags},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954515038&doi=10.1109%2fCTS.2010.5478494&partnerID=40&md5=fcbb170f2025c2bf7e69725738e4a3f0},
}

@InProceedings{liu_etal_2010,
  author        = {Liu, Kaipeng and Fang, Binxing and Zhang, Weizhe},
  title         = {Speak the Same Language with Your Friends: Augmenting Tag Recommenders with Social Relations},
  booktitle     = {Proceedings of the 21st ACM Conference on Hypertext and Hypermedia},
  year          = {2010},
  series        = {HT '10},
  pages         = {45--50},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1810627},
  doi           = {10.1145/1810617.1810627},
  isbn          = {978-1-4503-0041-4},
  keywords      = {personalization, social tagging, tag recommendation, rank1},
  location      = {Toronto, Ontario, Canada},
  numpages      = {6},
  url           = {http://doi.acm.org/10.1145/1810617.1810627},
}

@Article{liu_etal_2011,
  author        = {D. Liu and S. Yan and X. S. Hua and H. J. Zhang},
  title         = {Image Retagging Using Collaborative Tag Propagation},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2011},
  volume        = {13},
  number        = {4},
  pages         = {702-712},
  month         = {Aug},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Photo sharing websites such as Flickr host a massive amount of social images with user-provided tags. However, these tags are often imprecise and incomplete, which essentially limits tag-based image indexing and related applications. To tackle this issue, we propose an image retagging scheme that aims at refining the quality of the tags. The retagging process is formulated as a multiple graph-based multi-label learning problem, which simultaneously explores the visual content of the images, semantic correlation of the tags as well as the prior information provided by users. Different from classical single graph-based multi-label learning algorithms, the proposed algorithm propagates the information of each tag along an individual tag-specific similarity graph, which reflects the particular relationship among the images with respect to the specific tag and at the same time the propagations of different tags interact with each other in a collaborative way with an extra tag similarity graph. In particular, we present a robust tag-specific visual sub-vocabulary learning algorithm for the construction of those tag-specific graphs. Experimental results on two benchmark Flickr image datasets demonstrate the effectiveness of our proposed image retagging scheme. We also show the remarkable performance improvements brought by retagging in the task of image ranking.},
  doi           = {10.1109/TMM.2011.2134078},
  keywords      = {Web sites;graph theory;groupware;image retrieval;indexing;learning (artificial intelligence);vocabulary;Flickr image datasets;collaborative tag propagation;graph-based multilabel learning problem;image ranking;image retagging scheme;photo sharing Web sites;tag-based image indexing;tag-specific similarity graph;tag-specific visual sub-vocabulary learning algorithm;Collaboration;Correlation;Laplace equations;Optimization;Semantics;Tagging;Visualization;Image retagging;label propagation;multi-graph multi-label learning;semantic correlation},
}

@InProceedings{yao_wei_2011,
  author        = {D. Yao and J. Wei},
  title         = {Research on authority users tag recommendation based on divided topics},
  booktitle     = {2011 International Conference on Electronics, Communications and Control (ICECC)},
  year          = {2011},
  pages         = {1615-1618},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Currently, researches on the quality of social tagging, including problems triggered by synonymous tags, lack of hierarchy and standard structure, have become a hot issue in the academia. Based on the construction of resource-tag network, this paper uses CPM to identify users' interests and classify them into different topics. Then, user-resource network is constructed, and the key degree of each node is calculated. On basis of these, authority users in each topic are selected, and the most frequently used tags by these authority users are recommended to common users, in order to improve the quality of user tagging.},
  doi           = {10.1109/ICECC.2011.6066607},
  keywords      = {authorisation;classification;recommender systems;social networking (online);CPM;authority users tag recommendation;divided topics;hierarchy structure;resource-tag network;social tagging quality;standard structure;synonymous tags;user tagging quality;user-resource network;users interests;Communities;Computers;Educational institutions;Internet;Symmetric matrices;Tagging;Web pages;authority users;network;recommendation;social tagging},
}

@InProceedings{ustunbas_oeguducu_2011,
  author        = {Y. Ustunbas and S. G. Öguducu},
  title         = {A Recommendation Model for Social Resource Sharing Systems Based on Tripartite Graph Clustering},
  booktitle     = {2011 European Intelligence and Security Informatics Conference},
  year          = {2011},
  pages         = {378-381},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {The use of folksonomies to recommend web pages and tags assigned to these pages, is an important research direction in web recommendation. In this study, we implement a model that fits tripartite structure of folksonomies and extracts valuable information for generating recommendations. Then we developed two types of recommendation systems that take advantage of this information, web page recommendation and tag recommendation. We compared our recommendation results with the results using bipartite clustering of web pages and tags. The experiments are conducted on the data set obtained from Delicious web site. The results show that this model generates better accuracy results for web page recommendation while extracting more useful information simultaneously which could be an extra to generate different types of recommendations.},
  doi           = {10.1109/EISIC.2011.56},
  keywords      = {Web sites;graph theory;groupware;pattern clustering;recommender systems;Web pages;Web recommendation model;delicious Web site;folksonomies;social resource sharing systems;tag recommendation;tripartite graph clustering;Clustering algorithms;Data mining;Data models;Recommender systems;Tagging;Training;Web pages;folksonomy;recommendation model;social resource sharing system},
}

@InProceedings{lo_etal_2011,
  author        = {H. Y. Lo and J. C. Wang and H. M. Wang and S. D. Lin},
  title         = {Cost-sensitive stacking for audio tag annotation and retrieval},
  booktitle     = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2011},
  pages         = {2308-2311},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {Audio tags correspond to keywords that people use to de scribe different aspects of a music clip, such as the genre, mood, and instrumentation. Since social tags are usually as signed by people with different levels of musical knowledge, they inevitably contain noisy information. By treating the tag counts as costs, we can model the audio tagging problem as a cost-sensitive classification problem. In addition, tag correlation is another useful information for automatic audio tagging since some tags often co-occur. By considering the co-occurrences of tags, we can model the audio tagging problem as a multi-label classification problem. To exploit the tag count and correlation information jointly, we formulate the audio tagging task as a novel cost-sensitive multi-label (CSML) learning problem. The results of audio tag annotation and retrieval experiments demonstrate that the new approach outperforms our MIREX 2009 winning method.},
  doi           = {10.1109/ICASSP.2011.5946944},
  issn          = {1520-6149},
  keywords      = {audio signal processing;information retrieval;learning (artificial intelligence);music;signal classification;MIREX 2009 winning method;audio retrieval;audio tag annotation;cost-sensitive classification problem;cost-sensitive multilabel learning problem;cost-sensitive stacking;multilabel classification problem;music clip;tag correlation;Correlation;Feature extraction;Mood;Stacking;Support vector machines;Tagging;Training;Audio tag annotation;audio tag retrieval;cost-sensitive learning;multi-label;tag count, rank2},
}

@InProceedings{cai_etal_2011,
  author        = {J. Cai and H. Li and B. Lang},
  title         = {A Combined Music Label Propagation Model},
  booktitle     = {2011 Seventh International Conference on Computational Intelligence and Security},
  year          = {2011},
  pages         = {1251-1255},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Music labels, especially those related to high level semantics are very useful in music retrieval and recommendation, but normally hard to acquire. In the submission to ISMIR'07, Mohamed Sordo proposed a novel model, i.e., propagation of labels, to annotate music with existing labels, by using the content-based music similarity distance. In that model, a partially annotated collection with a lot of non-labeled music was annotated at a high precision and recall. In this paper, we proposed a new model -- label probability prediction model -- and introduce it into the Sordo's work, which makes a combined model, to improve the accuracy of propagation without exploiting any other information. In addition, we also made some modifications to the original Sordo's model that could make the algorithm works better. Then we compare the result of combined model to that yielded by the original on a publicly accessible ground truth data, and find that, the new approach can reach a higher recall. Furthermore, with the same recall, our method obtains a better precision.},
  doi           = {10.1109/CIS.2011.277},
  keywords      = {information retrieval;music;recommender systems;combined music label propagation model;content based music similarity distance;model label probability prediction model;music annotation;music recommendation;music retrieval;publicly accessible ground truth data;Computational modeling;Internet;Music;Music information retrieval;Prediction algorithms;Predictive models;Semantics;content-based similarity;music label;propagation model},
}

@InProceedings{cheng_wang_2011,
  author        = {Wengang Cheng and Xiaolei Wang},
  title         = {Image tag refinement using tag semantic and visual similarity},
  booktitle     = {Proceedings of 2011 International Conference on Computer Science and Network Technology},
  year          = {2011},
  volume        = {4},
  pages         = {2146-2149},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging on online websites provides users interfaces of describing resources with their own tags, and vast user-provided image tags facilitate image retrieval and management. However, these tags are often not related to the actual image content, affecting the performance of tag related applications. In this paper, a novel approach to automatically refine the image tags is proposed. Firstly, information entropy of the tag is defined to refine tag frequency to predict tag initial relevance. Then, tag correlation is calculated from two sides. One side is to measure semantic similarity of tag pairs using the structured information of the free encyclopedia Wikipedia. The other one is to compute the visual similarity of tag pairs based on the visual representation of the tag. Finally, to re-rank the original tags, a fast random walk with restart is used and the top ones are reserved as the final tags. Experimental results conducted on dataset NUS-WIDE demonstrate the promising effectiveness of our approach.},
  doi           = {10.1109/ICCSNT.2011.6182401},
  keywords      = {image retrieval;social networking (online);encyclopedia Wikipedia;image management;image retrieval;image tag refinement;information entropy;online websites;social tagging;tag semantic;visual similarity;semantic similarity;social tagging;tag refinement;visual similarity},
}

@InProceedings{panagakis_kotropoulos_2011a,
  author        = {Y. Panagakis and C. Kotropoulos},
  title         = {Automatic music tagging via PARAFAC2},
  booktitle     = {2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2011},
  pages         = {481-484},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic music tagging is addressed by resorting to auditory temporal modulations and Parallel Factor Analysis 2 (PARAFAC2). The starting point is to represent each music recording by its auditory temporal modulations. Then, an irregular third order tensor is formed. The first slice contains the vectorized training temporal modulations, while the second slice contains the corresponding multi-label vectors. The PARAFAC2 is employed to effectively harness the multi-label information for dimensionality reduction. Any vectorized test auditory representation of temporal modulations is first projected onto the semantic space derived via the PARAFAC2 and the coefficient vector is obtained. Then, the annotation vector is obtained by multiplying this coefficient vector by the left singular vectors of the second slice (i.e., the slice associated to the label vector). The proposed framework, outperforms the state-of-the-art auto-tagging systems, when applied to the CAL500 dataset in a 10-fold cross-validation experimental protocol.},
  doi           = {10.1109/ICASSP.2011.5946445},
  issn          = {1520-6149},
  keywords      = {audio recording;information retrieval;music;pattern classification;10-fold cross-validation experimental protocol;CAL500 dataset;PARAFAC2;auditory temporal modulation;automatic music tagging;autotagging system;dimensionality reduction;multilabel vector;music recording representation;parallel factor analysis 2;third order tensor;vectorized test auditory representation;vectorized training temporal modulation;Feature extraction;Modulation;Semantics;Tagging;Tensile stress;Training;Vectors;Automatic Music Tagging;Multi-label Classification;PARAFAC2;Tensor Decompositions, rank1},
}

@InProceedings{fang_etal_2011,
  author        = {Guannan Fang and Caixia Yuan and Xiaojie Wang and Jiang Li and Zhanjiang Song},
  title         = {From keywords to social tags: Tagging for dialogues},
  booktitle     = {2011 7th International Conference on Natural Language Processing and Knowledge Engineering},
  year          = {2011},
  pages         = {106-113},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {This paper proposes an unsupervised method for generating informative tags for multi-party dialogue in an open domain. Our model first extracts keywords from text through a multi-weighting framework, which includes frequency weighting, sentence weighting, speaker weighting and position weighting. Then we get their bigrams through frequent pattern matching. In order to generate more flexible and socialized tags, we expand keywords and their bigrams by exploring tag associations mined from a famous bookmarking web del.icio.us. Finally we rank the three parts of tag candidates under a uniform metric. Unlike previous models used for tag recommendation task, our model needs neither seed tags for source texts nor a complete tag set. It can recommend new tags out of the historical tag set. We evaluate our methods on 10,265 Chinese dialogues. Experimental results show our method outperform previous models like TextRank, TFIDF rank and KNN.},
  doi           = {10.1109/NLPKE.2011.6138177},
  keywords      = {interactive systems;social networking (online);KNN;TFIDF rank;TextRank;Web del.icio.us;bigrams;frequency weighting;informative tag generation;keyword extraction;multiparty dialogue;multiweighting framework;pattern matching;position weighting;sentence weighting;social tags;speaker weighting;Delta modulation;Electronic publishing;Linux;bigram;dialogue;multi-weighting;social tag;tag association},
}

@Article{feng_etal_2011,
  author        = {Songhe Feng and Hong Bao and Congyan Lang and De Xu},
  title         = {Combining visual attention model with multi-instance learning for tag ranking},
  journal       = {Neurocomputing},
  year          = {2011},
  volume        = {74},
  number        = {17},
  pages         = {3619 - 3627},
  issn          = {0925-2312},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2011.06.014},
  keywords      = {Image annotation, Tag ranking, Multi-instance learning, Visual attention model},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231211003791},
}

@Article{sun_etal_2011,
  author        = {Ke Sun and Xiaolong Wang and Chengjie Sun and Lei Lin},
  title         = {A language model approach for tag recommendation},
  journal       = {Expert Systems with Applications},
  year          = {2011},
  volume        = {38},
  number        = {3},
  pages         = {1575 - 1582},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2010.07.075},
  keywords      = {Tag recommendation, Language model for tag recommendation, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417410007037},
}

@Article{lipczak_milios_2011,
  author          = {Lipczak, M. and Milios, E.},
  title           = {Efficient tag recommendation for real-life data},
  journal         = {ACM Transactions on Intelligent Systems and Technology},
  year            = {2011},
  volume          = {3},
  number          = {1},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Despite all of the advantages of tags as an easy and flexible information management approach, tagging is a cumbersome task. A set of descriptive tags has to be manually entered by users whenever they post a resource. This process can be simplified by the use of tag recommendation systems. Their objective is to suggest potentially useful tags to the user. We present a hybrid tag recommendation system together with a scalable, highly efficient system architecture. The system is able to utilize user feedback to tune its parameters to specific characteristics of the underlying tagging system and adapt the recommendation models to newly added content. The evaluation of the system on six real-life datasets demonstrated the system's ability to combine tags from various sources (e.g., resource content or tags previously used by the user) to achieve the best quality of recommended tags. It also confirmed the importance of parameter tuning and content adaptation. A series of additional experiments allowed us to better understand the characteristics of the system and tagging datasets and to determine the potential areas for further system development. © 2011 ACM.},
  affiliation     = {Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada},
  art_number      = {2},
  author_keywords = {Broad folksonomies; Collaborative tagging; Folksonomies; Hybrid systems; Narrow folksonomies; Tag recommendation},
  document_type   = {Article},
  doi             = {10.1145/2036264.2036266},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2036264.2036266},
}

@Conference{chen_etal_2011,
  author          = {Chen, W. and Hsu, W. and Lee, M.L.},
  title           = {A unified framework for recommendations based on quaternary semantic analysis},
  year            = {2011},
  pages           = {1023-1032},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Social network systems such as FaceBook and YouTube have played a significant role in capturing both explicit and implicit user preferences for different items in the form of ratings and tags. This forms a quaternary relationship among users, items, tags and ratings. Existing systems have utilized only ternary relationships such as users-items-ratings, or users-items-tags to derive their recommendations. In this paper, we show that ternary relationships are insufficient to provide accurate recommendations. Instead, we model the quaternary relationship among users, items, tags and ratings as a 4-order tensor and cast the recommendation problem as a multi-way latent semantic analysis problem. A unified framework for user recommendation, item recommendation, tag recommendation and item rating prediction is proposed. The results of extensive experiments performed on a real world dataset demonstrate that our unified framework outperforms the state-of-the-art techniques in all the four recommendation tasks.},
  affiliation     = {School of Computing, National University of Singapore, Singapore, Singapore},
  author_keywords = {Collaborative tagging; Personalization; Recommendation; Social tags; Tensor factorization},
  document_type   = {Conference Paper},
  doi             = {10.1145/2009916.2010052},
  journal         = {SIGIR'11 - Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?id=2010052},
}

@Conference{djuana_etal_2011,
  author          = {Djuana, E. and Xu, Y. and Li, Y. and Jøsang, A.},
  title           = {Ontology learning from user tagging for tag recommendation making},
  booktitle       = {2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology},
  year            = {2011},
  volume          = {3},
  pages           = {310-313},
  month           = {Aug},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging into some form of ontology, but the application of the resulted ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved. © 2011 IEEE.},
  affiliation     = {Faculty of Science and Technology, Queensland University of Technology, Brisbane, Australia; Department of Informatics, University of Oslo, Oslo, Norway},
  art_number      = {60408567},
  author_keywords = {Collaborative tagging; Ontology learning; Tag recommendation},
  document_type   = {Conference Paper},
  doi             = {10.1109/WI-IAT.2011.163},
  journal         = {Proceedings - 2011 IEEE/WIC/ACM International Joint Conferences on Web Intelligence and Intelligent Agent Technology - Workshops, WI-IAT 2011},
  keywords        = {Internet;groupware;ontologies (artificial intelligence);recommender systems;vocabulary;Web;collaborative tagging;ontology learning;semantic ambiguity;tag ontology extraction;tag recommendation making;user tagging systems;Collaboration;Finite element methods;Ontologies;Recommender systems;Semantics;Tagging;Testing;collaborative tagging;ontology learning;tag recommendation, rank1},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/6040867/},
}

@Conference{jiang_2011,
  author          = {Jiang, B.},
  title           = {Improving collaborative tag recommendation by using local lexicon in social comment context},
  booktitle       = {Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design (CSCWD)},
  year            = {2011},
  pages           = {577-580},
  month           = {June},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Folksonomies enable Internet users to share, annotate and search for online resources with tags. Tag recommendation can suggest tags that maximize utility and help to promote resource sharing and collaboration. To date, little has been done to collaboratively recommend tags in narrow folksonomies [4]. In this paper we apply K-Nearest Neighbor to find similar users with common interests who lie close to each other in social networks. We present a novel tag recommendation strategy in social comment context. The evaluation shows that tag recommendation based on local lexicon is an effective way to improve collaborative resource sharing in social networks. © 2011 IEEE.},
  affiliation     = {School of Computer and Information Engineering, Zhejiang Gongshang University, Hangzhou, China},
  art_number      = {5960130},
  author_keywords = {Collaborative Tagging; Folksonomies; Social Networks; Tag Co-occurrence; Tag Recommendation},
  document_type   = {Conference Paper},
  doi             = {10.1109/CSCWD.2011.5960130},
  journal         = {Proceedings of the 2011 15th International Conference on Computer Supported Cooperative Work in Design, CSCWD 2011},
  keywords        = {Internet;recommender systems;social networking (online);Internet;collaborative resource sharing;collaborative tag recommendation;folksonomies;k-nearest neighbor;local lexicon;social comment contex;social networks;Collaboration;Context;Media;Presses;Social network services;Tagging;Vocabulary;Collaborative Tagging;Folksonomies;Social Networks;Tag Co-occurrence;Tag Recommendation},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/5960130/},
}

@Conference{kohi_etal_2011,
  author          = {Kohi, A. and Ebrahimi, S.J. and Jalali, M.},
  title           = {Improving the accuracy and efficiency of tag recommendation system by applying hybrid methods},
  booktitle       = {2011 1st International eConference on Computer and Knowledge Engineering (ICCKE)},
  year            = {2011},
  pages           = {242-248},
  month           = {Oct},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Recently applications of social tagging systems have increased. These systems allow users to organize, manage and search the required resource freely, thus by combination and integration of recommendation systems in social software, assisting users to appropriately assign tag to resources and try to improve annotation among users. The challenges of recommendation systems are large-scale data, inconsistence data, usage of time-consuming machine learning algorithms, long and unreasonable time of recommendation and not being scalable to the demands of real world applications. Recently more efforts have been conducted to solve these problems. In this paper we proposed a tag recommendation system that is able to work with large-scale data and being applied in real world. The proposed system's evaluation performed on a dataset collected from Delicious.com. The results demonstrated the efficiency and accuracy of proposed system. © 2011 IEEE.},
  affiliation     = {Department of Software Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran; Department of Mathematical Sciences, Mashhad Branch, Islamic Azad University, Mashhad, Iran},
  art_number      = {6413358},
  author_keywords = {collaborative tagging system; collaborative-based; folksonomies; recommendation system; social tagging system; tag recommender},
  document_type   = {Conference Paper},
  doi             = {10.1109/ICCKE.2011.6413358},
  journal         = {2011 1st International eConference on Computer and Knowledge Engineering, ICCKE 2011},
  keywords        = {learning (artificial intelligence);recommender systems;social networking (online);Delicious.com;hybrid methods;inconsistence data;large-scale data;social software;social tagging systems;tag recommendation system;time-consuming machine learning algorithms;Accuracy;Crawlers;Data mining;Databases;Measurement;Tagging;Web pages;collaborative tagging system;collaborative-based;folksonomies;recommendation system;social tagging system;tag recommender},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/6413358/},
}

@InProceedings{hu_etal_2012,
  author        = {R. Hu and T. He and F. Li and P. Hu},
  title         = {Tag recommendation based on tag-topic model},
  booktitle     = {2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems},
  year          = {2012},
  volume        = {03},
  pages         = {1501-1505},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {With the rapid increase of the social websites most social tagging systems are allowing users to share and to label various kinds of resources with their favorite tags. However the uncontrolled use of tags makes the resources attached with some irrelevant even noise tags. To solve the problem this paper proposes a tag-topic model based approach to recommend tags for resources which elicits latent topics from resources and maps new resources to these latent topics so as to recommend the most appropriate tags for the resources. The experimental results show the effectiveness of the proposed approach.},
  doi           = {10.1109/CCIS.2012.6664635},
  issn          = {2376-5933},
  keywords      = {recommender systems;social networking (online);latent topics;social Websites;social tagging systems;tag recommendation;tag-topic model;Blogs;Collaboration;Educational institutions;Noise;Resource management;Semantics;Tagging;tag recommendation;tag-topic model},
}

@InProceedings{xu_etal_2012,
  author        = {D. Xu and Z. Wang and Y. Zhang and P. Zong},
  title         = {A Collaborative Tag Recommendation Based on User Profile},
  booktitle     = {2012 4th International Conference on Intelligent Human-Machine Systems and Cybernetics},
  year          = {2012},
  volume        = {1},
  pages         = {331-334},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {With the increasing popularity of social tagging, services that assist the user in the task of tagging, such as tag recommenders, are more and more required. As we all known, crucial to the performance of a recommendation system is the accuracy of the user profiles used to represent the interests of the users. We propose a tag recommendation based on user profile which represents user preferences by taking user's ranking pairwise tag preferences. Although the dataset is obtained indirectly, the experiments show that the tag recommendation based on the proposed user profile has outperformed the baseline tag recommendation.},
  doi           = {10.1109/IHMSC.2012.89},
  keywords      = {collaborative filtering;recommender systems;social networking (online);user modelling;collaborative tag recommendation;pairwise tag preference ranking;recommendation system performance;social tagging;user preference representation;user profile;Collaboration;Communities;Educational institutions;Learning systems;Recommender systems;Tagging;Vectors;personalized tag rcommendation;user prferences;user profile},
}

@InProceedings{wang_etal_2012b,
  author        = {S. Wang and S. Jiang and Q. Huang and Q. Tian},
  title         = {Multi-feature metric learning with knowledge transfer among semantics and social tagging},
  booktitle     = {2012 IEEE Conference on Computer Vision and Pattern Recognition},
  year          = {2012},
  pages         = {2240-2247},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Previous metric learning approaches learn a unified metric for all the classes on single feature representation, thus cannot be directly transplanted to applications involving multiple features, hundreds to thousands of hierarchical structured semantics and abundant social tagging. In this paper, we propose a novel multi-task multi-feature metric learning method which models the information sharing mechanism among different learning tasks. We decompose the real world multi-class problems such as semantic categorization or automatic tagging into a set of tasks where each task corresponds to several classes with strong visual correlation. We conduct metric learning to learn a set of (hyper)category-specific metrics for all the tasks. By encouraging model sharing among tasks, more generalization power is acquired. Another advantage is the capability of simultaneous learning with semantic information and social tagging based on the multi-task learning framework, and thus they both benefit from the information provided by each other. Experiments demonstrate the advantages on applications including semantic categorization and automatic tagging compared with other popular metric learning approaches.},
  doi           = {10.1109/CVPR.2012.6247933},
  issn          = {1063-6919},
  keywords      = {computer vision;learning (artificial intelligence);automatic tagging;category-specific metrics;feature representation;hierarchical structured semantics;information sharing mechanism;knowledge transfer;multifeature metric learning;multitask metric learning;semantic categorization;semantic information;social tagging;visual correlation;Kernel;Measurement;Semantics;Support vector machines;Tagging;Training;Visualization},
}

@InProceedings{xu_etal_2012a,
  author        = {T. Xu and D. Liu and E. Chen and H. Cao and J. Tian},
  title         = {Towards Annotating Media Contents through Social Diffusion Analysis},
  booktitle     = {2012 IEEE 12th International Conference on Data Mining},
  year          = {2012},
  pages         = {1158-1163},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Recently, the boom of media contents on the Internet raises challenges in managing them effectively and thus requires automatic media annotation techniques. Motivated by the observation that media contents are usually shared frequently in online communities and thus have a lot of social diffusion records, we propose a novel media annotating approach depending on these social diffusion records instead of metadata. The basic assumption is that the social diffusion records reflect the common interests (CI) between users, which can be analyzed for generating annotations. With this assumption, we present a novel CI-based social diffusion model and translate the automatic annotating task into the CI-based diffusion maximization (CIDM) problem. Moreover, we propose to solve the CIDM problem through two optimization tasks, corresponding to the training and test stages in supervised learning. Extensive experiments on real-world data sets show that our approach can effectively generate high quality annotations, and thus demonstrate the capability of social diffusion analysis in annotating media.},
  doi           = {10.1109/ICDM.2012.23},
  issn          = {1550-4786},
  keywords      = {Internet;content management;learning (artificial intelligence);optimisation;social networking (online);CI-based diffusion maximization problem;CI-based social diffusion model;CIDM problem;Internet;automatic media content annotation techniques;common interests;online community;optimization tasks;social diffusion analysis;social diffusion records;supervised learning;test stage;training stage;Computational modeling;Data mining;Feature extraction;Media;Motion pictures;Optimization;Training;Automatic annotation;common interests;diffusion maximization;social diffusion;social media, rank1},
}

@InProceedings{liu_etal_2012,
  author        = {Y. Liu and N. Yang and G. Yang},
  title         = {An Approach for Personalized Tag Recommendation Based on Interest Transfer Model},
  booktitle     = {2012 Ninth Web Information Systems and Applications Conference},
  year          = {2012},
  pages         = {71-74},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Recently, social tagging systems become more and more popular in many Web 2.0 applications. In such systems, Users are allowed to annotate a particular resource with a freely chosen a set of tags. These user-generated tags can represent users' interests more concise and closer to human understanding. Interests will change over time. Thus, how to describe users' interests and interests transfer path become a big challenge for personalized recommendation systems. In this approach, we propose a variable-length time interval division algorithm and user interest model based on time interval. Then, in order to draw users' interests transfer path over a specific time period, we suggest interest transfer model. After that, we apply a classical community partition algorithm in our approach to separate users into communities. Finally, we raise a novel method to measure users' similarities based on interest transfer model and provide personalized tag recommendation according to similar users' interests in their next time intervals. Experimental results demonstrate the higher precision and recall with our approach than classical user-based collaborative filtering methods.},
  doi           = {10.1109/WISA.2012.43},
  keywords      = {recommender systems;user interfaces;Web 2.0 application;community partition algorithm;interest transfer model;personalized recommendation system;personalized tag recommendation;social tagging system;user interest;user similarity;user-based collaborative filtering method;user-generated tag;variable-length time interval division algorithm;Collaboration;Computational modeling;Educational institutions;Filtering;Heuristic algorithms;Mathematical model;Tagging;Interest Transfer Model;Time Interval;User Interest Model;User Interests},
}

@InProceedings{yang_etal_2012,
  author        = {T. Yang and Y. Cui and Y. Jin and M. Song},
  title         = {A new algorithm for multi-mode recommendations in social tagging systems},
  booktitle     = {2012 IEEE 2nd International Conference on Cloud Computing and Intelligence Systems},
  year          = {2012},
  volume        = {02},
  pages         = {696-700},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is one of the most important characteristics of web 2.0 services. Different from traditional recommendation algorithms, in social tagging systems, recommendation algorithms involve the ternary relations between users, items and tags. And algorithms that support integrated multi-mode recommendations are very appealing. We propose a multi-mode recommendation algorithm based on higher-order singular value decomposition, and our algorithm handles not only the existing triplets {user, item, tag}, but also the pairs {user, item} with no tags in social tagging system. Meanwhile. We propose a measure for user recommendations. We empirically show that our algorithm outperforms a state-of-the-art algorithm for multi-mode recommendations with a Last.fm dataset.},
  doi           = {10.1109/CCIS.2012.6664264},
  issn          = {2376-5933},
  keywords      = {Web services;probability;recommender systems;singular value decomposition;social networking (online);Last.fm dataset;Web 2.0 services;higher-order singular value decomposition;multimode recommendation algorithms;social tagging systems;ternary relations;Accuracy;Approximation methods;Entropy;Matrix decomposition;Recommender systems;Tagging;Tensile stress;Multi-recommendation;Social tagging systems;Tensor factorization},
}

@InProceedings{hmimida_ankoud_2012,
  author        = {M. Hmimida and M. Ankoud},
  title         = {Recommendation system for documentary classification},
  booktitle     = {International Conference on Education and e-Learning Innovations},
  year          = {2012},
  pages         = {1-5},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {In the context of the NAR project “Miipa-doc”, we develop a new type of Knowledge Organization System (KOS) called Hypertagging based on the tagging of electronic documents and the principles of faceted classification. It was designed to simplify the tasks of information management for the organizations' staff. In this paper, we propose a new recommendation model and algorithm which are based on a faceted classification by level in the aim to facilitate the documents' indexing. This approach exploits the user trace indexing of his/her documents to learn about the user preferences and then to produce their recommendations. Consequently, these recommendations will provide a kind of knowledge base aiming at improving document ranking and highlight most relevant information that meeting user needs. This model is based on a statistical method called Association Rules (AR) using an Apriori algorithm to generate the recommendations.},
  doi           = {10.1109/ICEELI.2012.6360654},
  keywords      = {document handling;information filtering;pattern classification;recommender systems;statistical analysis;AR;Apriori algorithm;KOS;Miipa doc;NAR project;association rules;document ranking;documentary classification;electronic documents;information filtering;information management;knowledge organization system;recommendation system;statistical method;Association rules;Context;Correlation;Filtering;Indexing;Itemsets;Tagging;Hypertagging;KOS;Recommendation;association rules;facet;level;tag},
}

@InProceedings{alper_oegueduecue_2012,
  author        = {M. E. Alper and S. G. Ögüdücü},
  title         = {Personalized Recommendation in Folksonomies Using a Joint Probabilistic Model of Users, Resources and Tags},
  booktitle     = {2012 11th International Conference on Machine Learning and Applications},
  year          = {2012},
  volume        = {1},
  pages         = {368-373},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {The concept of Web 2.0 or "semantic web" has been getting more and more popular during the last half decade. The potential of very subtle yet important emergent semantics hidden in such environments calls for equally elegant and powerful methods to "mine" them. However, much of the previous work on model based recommender systems for folksonomies considered user to resource and resource to tag similarity separately, ignoring the dependency of users' interest to both the tags and the corresponding resources. In this paper, we propose a probabilistic personalized recommendation model, Latent Interest Model, that accounts for users, tags and resources jointly. The proposed method's performance is evaluated on real data sets obtained from a popular online bookmarking site using different performance measures for tag and resource recommendation tasks. Our experimental results show that our model captures personal preferences for tag usage and resource selection. Performance evaluation of Latent Interest Model indicates that the proposed personalized method yields significant improvement of recommendation accuracy.},
  doi           = {10.1109/ICMLA.2012.68},
  keywords      = {identification technology;recommender systems;resource allocation;semantic Web;Web 2.0;emergent semantics;environments calls;folksonomies;joint probabilistic model;latent interest model performance evaluation;model-based recommender systems;performance measures;personal preferences;popular online bookmarking site;probabilistic personalized recommendation model;real data sets;resource recommendation tasks;resource selection;semantic Web;tag usage;Computational modeling;Context;Context modeling;Data models;Probabilistic logic;Tagging;Vectors;folksonomy;personalized recommendation},
}

@InProceedings{wang_etal_2012a,
  author        = {J. C. Wang and H. M. Wang and S. K. Jeng},
  title         = {Playing with tagging: A real-time tagging music player},
  booktitle     = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2012},
  pages         = {77-80},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {Visualizing audio signals during playback has long been a fundamental function of music players. However, most visual effects are generated by audio signal processing directly and render meaningless or incomprehensible displays to users. In this paper, we present an intelligent music player called the Playing with Tagging (PWT) music player. By integrating a real-time music tagger, the PWT player can display dynamic tag distributions via a set of tag bars that move in sync with the music. To synchronize the tag distributions, the music tagger must be able to online recognize the music tags. We utilize a Gaussian mixture model (GMM) as an auditory feature encoding reference and a mixture of tag-based aspect models (TBAMs) to predict the tag distribution for a short sliding chunk of the music played. To evaluate the real-time tagging function, we simulate tag prediction on short music chunks. The results of experiments on the MajorMiner dataset demonstrate the potential and effectiveness of the proposed music tagging method.},
  doi           = {10.1109/ICASSP.2012.6287821},
  issn          = {1520-6149},
  keywords      = {Gaussian processes;audio coding;GMM;Gaussian mixture model;MajorMiner dataset;PWT music player;TBAM;audio signal processing;auditory feature encoding reference;intelligent music player;real-time tagging music player;short music chunks;short sliding chunk;tag-based aspect models;Feature extraction;Music;Real time systems;Synchronization;Tagging;Training;Vectors;Online music tag annotation;probabilistic tag-based aspect model;real-time music visualization, rank1},
}

@InProceedings{gao_li_2012,
  author        = {Y. Gao and B. Li},
  title         = {Active learning for tag recommendation utilizing on-line photos lacking tags},
  booktitle     = {2012 19th IEEE International Conference on Image Processing},
  year          = {2012},
  pages         = {2869-2872},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Recommending text tags for on-line photos is useful for Internet photo services. Typical solutions to this problem require analysis of the correlation among different attributes of the photos, including the correlation between the textual features and visual features computed from a photo. However, most on-line photos have very few tags or even no tags, and thus they contribute little or none to the analysis of tag-photo correlation, which is a key component in those schemes that rely on such analysis for tag recommendation. To address this practical challenge, we propose an active learning method for incorporating photos with no or few tags so as to enhance the correlation analysis for improved performance in tag recommendation. We demonstrate the effectiveness of the proposed approach using a dataset of more than 33,000 photos collected from Flickr.},
  doi           = {10.1109/ICIP.2012.6467498},
  issn          = {1522-4880},
  keywords      = {Internet;learning (artificial intelligence);recommender systems;Flickr;Internet photo services;active learning method;online photos;tag recommendation;text tags;Computational modeling;Correlation;Measurement;Semantics;Tagging;Training;Visualization;Tag recommendation;active learning},
}

@Article{miotto_lanckriet_2012,
  author        = {R. Miotto and G. Lanckriet},
  title         = {A Generative Context Model for Semantic Music Annotation and Retrieval},
  journal       = {IEEE Transactions on Audio, Speech, and Language Processing},
  year          = {2012},
  volume        = {20},
  number        = {4},
  pages         = {1096-1108},
  month         = {May},
  issn          = {1558-7916},
  __markedentry = {[felipe:1]},
  abstract      = {While a listener may derive semantic associations for audio clips from direct auditory cues (e.g., hearing “bass guitar”) as well as from “context” (e.g., inferring “bass guitar” in the context of a “rock” song), most state-of-the-art systems for automatic music annotation ignore this context. Indeed, although contextual relationships correlate tags, many auto-taggers model tags independently. This paper presents a novel, generative approach to improve automatic music annotation by modeling contextual relationships between tags. A Dirichlet mixture model (DMM) is proposed as a second, additional stage in the modeling process, to supplement any auto-tagging system that generates a semantic multinomial (SMN) over a vocabulary of tags when annotating a song. For each tag in the vocabulary, a DMM captures the broader context the tag defines by modeling tag co-occurrence patterns in the SMNs of songs associated with the tag. When annotating songs, the DMMs refine SMN annotations by leveraging contextual evidence. Experimental results demonstrate the benefits of combining a variety of auto-taggers with this generative context model. It generally outperforms other approaches to modeling context as well.},
  doi           = {10.1109/TASL.2011.2172423},
  keywords      = {audio signal processing;information retrieval;music;polynomials;Dirichlet mixture model;audio clips;auto-taggers model;automatic music annotation;contextual relationship modeling;direct auditory cues;generative context model;semantic associations;semantic multinomial;semantic music annotation;semantic music retrieval;song annotation;tag vocabulary;Acoustics;Context;Context modeling;Correlation;Rocks;Semantics;Vocabulary;Audio annotation and retrieval;Dirichlet mixture models;context modeling;music information retrieval, rank1},
}

@InProceedings{wang_etal_2012,
  author        = {H. Wang and D. Joshi and J. Luo and H. Huang and M. Park},
  title         = {Simultaneous Image Annotation and Geo-Tag Prediction via Correlation Guided Multi-task Learning},
  booktitle     = {2012 IEEE International Symposium on Multimedia},
  year          = {2012},
  pages         = {69-72},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {In recent years, several methods have been proposed to exploit image context (such as location) that provides valuable cues complementary to the image content, i.e., image annotations and geotags have been shown to assist the prediction of each other. To exploit the useful interrelatedness between these two heterogeneous prediction tasks, we propose a new correlation guided structured sparse multi-task learning method. We utilize a joint classification and regression model to identify annotation-informative and geotag-relevant image features. We also introduce the tree-structured sparsity regularizations into multi-task learning to integrate the label correlations in multi-label image annotation. Finally we derive an efficient algorithm to optimize our non-smooth objective function. We demonstrate the performance of our method on three real-world geotagged multi-label image data sets for both semantic annotation and geotag prediction.},
  doi           = {10.1109/ISM.2012.21},
  keywords      = {correlation methods;feature extraction;image classification;learning (artificial intelligence);regression analysis;tree data structures;annotation-informative image feature;classification;correlation guided structured sparse multitask learning method;feature selection;geotag prediction;geotag-relevant image feature;heterogeneous prediction task;image content;image context;image location;label correlation;multilabel image annotation;nonsmooth objective function;regression model;semantic annotation;tree-structured sparsity regularization;Context;Correlation;Global Positioning System;Joints;Multimedia communication;Prediction algorithms;Semantics;feature selection;geotag;multi-task learning},
}

@InProceedings{chidlovskii_2012,
  author        = {B. Chidlovskii},
  title         = {Tag Ranking by Linear Relational Neighbourhood Propagation},
  booktitle     = {2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining},
  year          = {2012},
  pages         = {184-188},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {We propose a tag recommendation method which can assist users in tagging process by suggesting relevant tags. The method is based on query-based ranking on relational multi-type graphs which capture the annotation relationship between objects and tags, as well as the object similarity and tag correlation. The additional advance consists in extending the linear neighbourhood propagation to the relational graphs with the Laplacian regularization framework. We report evaluation results on a large-scale Flickr data set.},
  doi           = {10.1109/ASONAM.2012.40},
  keywords      = {graph theory;query processing;recommender systems;social networking (online);Laplacian regularization framework;large-scale Flickr data set;linear relational neighbourhood propagation;object similarity;object-tag annotation relationship;query-based ranking;relational graphs;relational multitype graphs;tag correlation;tag ranking;tag recommendation method;tagging process;Correlation;Image edge detection;Image reconstruction;Laplace equations;Minimization;Tagging;Vectors;Laplacian regularization;Tag ranking;linear neighbourhood propagation;relational graphs},
}

@InProceedings{panagakis_kotropoulos_2012,
  author        = {Y. Panagakis and C. Kotropoulos},
  title         = {Automatic music tagging by low-rank representation},
  booktitle     = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2012},
  pages         = {497-500},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {A novel multi-label annotation method is proposed and applied to music tagging. Each music recording is represented by its auditory temporal modulations (ATMs). Given a set of training music recordings represented by the tag-music recording matrix having zero-one (indicator) vectors of the tags associated with each recording in its columns along with the matrix of the ATM representations in its columns, a low-rank weight matrix is sought, such that the tag-music recording matrix is expressed as the product of the weight matrix and the matrix of the ATM representations plus an error matrix. Clearly, such a weight matrix captures the relationships between the labels (i.e., tags) and the audio features. It can be derived by solving a convex nuclear norm minimization problem, if the tag-music recording matrix and the matrix of the ATM representations are assumed to be jointly low-rank. Having found the weight matrix, the annotation vector for labeling any test music recording can be obtained by multiplying the weight matrix with its ATM representation. The just outlined method is referred to as low-rank representation based multi-label annotation (LRRMA). The LRRMA outperforms the state-of-the-art auto-tagging systems, when applied to the CAL500 dataset in a 5-fold cross-validation experimental protocol.},
  doi           = {10.1109/ICASSP.2012.6287925},
  issn          = {1520-6149},
  keywords      = {audio signal processing;matrix algebra;minimisation;music;LRRMA;annotation vector;auditory temporal modulations;automatic music tagging;autotagging systems;convex nuclear norm minimization problem;error matrix;low-rank representation;low-rank weight matrix;multilabel annotation;tag-music recording matrix;zero-one vectors;Minimization;Modulation;Semantics;Sparse matrices;Tagging;Training;Vectors;Automatic Music Tagging;Low-Rank Representation;Multi-label Classification;Nuclear Norm Minimization},
}

@Article{sevillano_etal_2012,
  author        = {X. Sevillano and T. Piatrik and K. Chandramouli and Q. Zhang and E. Izquierdo},
  title         = {Indexing Large Online Multimedia Repositories Using Semantic Expansion and Visual Analysis},
  journal       = {IEEE MultiMedia},
  year          = {2012},
  volume        = {19},
  number        = {3},
  pages         = {53-61},
  month         = {July},
  issn          = {1070-986X},
  __markedentry = {[felipe:1]},
  abstract      = {The proposed framework automatically predicts user tags for online videos from their visual features and associated textual metadata, which is semantically expanded using complementary textual resources.},
  doi           = {10.1109/MMUL.2012.28},
  keywords      = {indexing;information resources;meta data;multimedia computing;semantic Web;video signal processing;associated textual metadata;complementary textual resources;large online multimedia repository indexing;online videos;semantic expansion;user tag prediction;visual analysis;visual features;Electronic publishing;Feature extraction;Indexing;Internet;Prediction models;Semantics;Tagging;Videos;Visualization;MediaEval;bag-of-articles;geotagging;indexing;large-scale multimedia data;multimedia;semantic expansion;tagging;visual similarity},
}

@InProceedings{vijay_jacob_2012,
  author        = {V. Vijay and I. Jeena Jacob},
  title         = {Combined approach of user specified tags and content-based image annotation},
  booktitle     = {2012 International Conference on Devices, Circuits and Systems (ICDCS)},
  year          = {2012},
  pages         = {162-166},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {The availability of large quantities of user contributed images with labels has provided opportunities to develop automatic tools to tag images to facilitate image search and retrieval. This paper discusses about an approach for automatic annotation in digital images. Some of the previous models for automatic image annotations are translation model (TM), continuous-space relevance model (CRM) and multiple Bernoulli relevance model (MBRM).These models have some semantic gap problems. To avoid these problems here developed a hybrid probabilistic model (HPM) which is used to combine both low-level image features and user provided tags to automatically tag images. For images without any tags, HPM predicts new tags based on the low-level image features. Low-level features are color, texture and shape. For images with user provided tags, HPM use both the image features and the tags to recommend additional tags to label the images. Here a Colored Pattern Appearance Model (CPAM) is used to capture both color and texture information. An L1 norm kernel method is used to estimate the correlations between image features and semantic concepts. The kernel density estimation is accelerated by an Improved Fast Gauss transform(IFGT).When the number of images becomes larger then Tag-Image Association Matrix (TIAM) used in the HPM framework become very sparse, thus it is very difficult to estimate tag-to-tag co-occurrence probabilities. So a collaborative filtering method based on nonnegative matrix factorization (NMF) is used for tackling this data sparsity issue. Here a CF algorithm is used to find the correlation between the words. Building such a HPM will make image labelling more efficient and less labour intensive.},
  doi           = {10.1109/ICDCSyst.2012.6188696},
  keywords      = {Gaussian processes;collaborative filtering;content-based retrieval;estimation theory;feature extraction;image colour analysis;image retrieval;image texture;matrix decomposition;transforms;CPAM;CRM;HPM framework;IFGT;L1 norm kernel method;MBRM;NMF;TIAM;TM;automatic image annotations;automatic tools;collaborative filtering method;colored pattern appearance model;content-based image annotation;continuous-space relevance model;correlation estimation;data sparsity issue;hybrid probabilistic model;image labelling;image search;improved fast Gauss transform;kernel density estimation;low-level image features;multiple Bernoulli relevance model;nonnegative matrix factorization;semantic concepts;semantic gap problems;tag-image association matrix;tag-to-tag co-occurrence probability estimation;translation model;user specified tags;Computational modeling;Correlation;Estimation;Kernel;Training;Automatic image annotation;collaborative filtering;feature extraction;kernel density estimation;non-negative matrix factorization},
}

@InProceedings{nam_etal_2012,
  author        = {H. H. Nam and H. Hachiya and M. Sugiyama},
  title         = {Computationally efficient multi-label classification by least-squares probabilistic classifier},
  booktitle     = {2012 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2012},
  pages         = {2077-2080},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {Multi-label classification allows a sample to belong to multiple classes simultaneously, which is often the case in real-world applications such as audio tagging, image annotation, video search, and text mining. In such a multi-label scenario, taking into account correlation between multiple labels can boost the classification accuracy. However, this in turn makes classifier training more challenging because handling multiple labels tends to induce a high-dimensional optimization problem. In this paper, we propose a highly scalable multi-label classifier based on a computationally efficient classification algorithm called the least-squares probabilistic classifier. Through experiments, we show the usefulness of our proposed method.},
  doi           = {10.1109/ICASSP.2012.6288319},
  issn          = {1520-6149},
  keywords      = {classification;data mining;audio tagging;classification accuracy;high dimensional optimization problem;image annotation;least squares probabilistic classifier;multilabel classification algorithm;multiple labels;scalable multilabel classifier;text mining;video search;Correlation;Equations;Optimization;Probabilistic logic;Tagging;Training;Vectors;Ψ Ψ⊤;Freesound;Least-Squares Probabilistic Classifier;Multi-Label Classification},
}

@InProceedings{fan_etal_2012,
  author        = {M. Fan and Q. Zhou and T. F. Zheng},
  title         = {Content-Based Semantic Tag Ranking for Recommendation},
  booktitle     = {2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology},
  year          = {2012},
  volume        = {1},
  pages         = {292-296},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Content-based social tagging recommendation, which considers the relationship between the tags and the descriptions contained in resources, is proposed to remedy the cold-start problem of collaborative filtering. There is such a common phenomenon that certain tag does not appear in the corresponding description, however, they do semantically relate with each other. State-of-the-art methods seldom consider this phenomenon and thus still need to be improved. In this paper, we propose a novel content-based social tag ranking scheme, aiming to recommend the semantic tags that the descriptions may not contain. The scheme firstly acquires the quantized semantic relationships between words with empirical methods, then constructs the weighted tag-digraph based on the descriptions and acquired quantized semantics, and finally performs a modified graph-based ranking algorithm to refine the score of each candidate tag for recommendation. Experimental results on both English and Chinese datasets show that the proposed scheme performs better than several state-of-the-art content-based methods.},
  doi           = {10.1109/WI-IAT.2012.32},
  keywords      = {collaborative filtering;content management;graph theory;identification technology;recommender systems;social sciences;Chinese datasets;English datasets;cold-start problem;collaborative filtering;content-based semantic tag ranking;content-based social tag ranking scheme;content-based social tagging recommendation;description-based weighted tag-digraph;modified graph-based ranking algorithm;quantized semantics;semantic relationships;ranking;recommender system;social tagging},
}

@Article{christidis_etal_2012,
  author        = {Konstantinos Christidis and Gregoris Mentzas and Dimitris Apostolou},
  title         = {Using latent topics to enhance search and recommendation in Enterprise Social Software},
  journal       = {Expert Systems with Applications},
  year          = {2012},
  volume        = {39},
  number        = {10},
  pages         = {9297 - 9307},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2012.02.073},
  keywords      = {Enterprise Social Software, Search, Recommender systems, Latent topic models, Latent Dirichlet Allocation},
  url           = {http://www.sciencedirect.com/science/article/pii/S095741741200317X},
}

@Article{zhang_etal_2012,
  author        = {Yin Zhang and Bin Zhang and Kening Gao and Pengwei Guo and Daming Sun},
  title         = {Combining content and relation analysis for recommendation in social tagging systems},
  journal       = {Physica A: Statistical Mechanics and its Applications},
  year          = {2012},
  volume        = {391},
  number        = {22},
  pages         = {5759 - 5768},
  issn          = {0378-4371},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.physa.2012.05.013},
  keywords      = {Social tagging, Recommendation, Hybrid model, Latent Dirichlet allocation, Infophysics},
  url           = {http://www.sciencedirect.com/science/article/pii/S0378437112003846},
}

@Conference{font_etal_2012,
  author        = {Font, F. and Serrà, J. and Serra, X.},
  title         = {Folksonomy-based tag recommendation for online audio clip sharing},
  year          = {2012},
  pages         = {73-78},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging has emerged as an efficient way to semantically describe online resources shared by a community of users. However, tag descriptions present some drawbacks such as tag scarcity or concept inconsistencies. In these situations, tag recommendation strategies can help users in adding meaningful tags to the resources being described. Freesound is an online audio clip sharing site that uses collaborative tagging to describe a collection of more than 140, 000 sound samples. In this paper we propose four algorithm variants for tag recommendation based on tag co-occurrence in the Freesound folksonomy. On the basis of removing a number of tags that have to be later predicted by the algorithms, we find that using ranks instead of raw tag similarities produces statistically significant improvements. Moreover, we show how specific strategies for selecting the appropriate number of tags to be recommended can significantly improve algorithms' performance. These two aspects provide insight into some of the most basic components of tag recommendation systems, and we plan to exploit them in future real-world deployments. © 2012 International Society for Music Information Retrieval.},
  affiliation   = {Music Technology Gorup, Universitat Pompeu Fabra, Barcelona, Spain; Artificial Intelligence Research Institute (IIIA-CSIC), Bellaterra, Barcelona, Spain},
  document_type = {Conference Paper},
  journal       = {Proceedings of the 13th International Society for Music Information Retrieval Conference, ISMIR 2012},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873460515&partnerID=40&md5=b10ea431818a860622a7f614e2f23a88},
}

@Article{mistry_sen_2012,
  author        = {Mistry, O. and Sen, S.},
  title         = {Probabilistic approaches to tag recommendation in a social bookmarking network},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2012},
  volume        = {7057 LNAI},
  pages         = {270-287},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Tagging has become increasingly popular with the explosion of user-created content on the web. A 'tag' can be defined as a group of keywords that makes organizing, browsing and searching for content more efficient. Users apply tags to a variety of web-based, shareable content including photos, videos, news articles, bookmarks, friends, etc. Tag suggestions for blog posts or web-pages have changed the focus of the tagging process from generation to recognition, thus making it less time and effort intensive. We propose tag recommendation algorithms for personalized agents, that recommend tags for bookmarks stored in a popular social bookmarking website, Del.ici.ous [6]. Our tag recommender agents learn to classify the tags according to their semantic similarity based on collaborative tagging by the users. Hence this approach can be used to facilitate folksonomy formation for the social network. In this paper, we first empirically verify our hypothesis that web pages with similar content are tagged with similar tags. We compare both Content-based and Collaborative approaches to recommend tags to the users. We analyze the performance of two probabilistic approaches to recommend tags from users with similar tagging behavior. © Springer-Verlag Berlin Heidelberg 2012.},
  affiliation   = {University of Tulsa, 800 South Tucker Avenue, Tulsa, OK 74104, United States},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-642-25920-3_19},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887217579&doi=10.1007%2f978-3-642-25920-3_19&partnerID=40&md5=dbe3fc59257dbe75e8a5ce007bed655c},
}

@Conference{djuana_etal_2012,
  author          = {Djuana, E. and Xu, Y. and Li, Y. and Cox, C.},
  title           = {Personalization in tag ontology learning for recommendation making},
  year            = {2012},
  pages           = {368-377},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users' information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users' tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation. © 2012 ACM.},
  affiliation     = {School of Electrical Engineering and Computer Science, Queensland University of Technology, GPO Box 2434, Brisbane, QLD 4001, Australia; Rummble Labs., Rummble Ltd., 1 East Poultry Avenue, London, EC1A 9PT, United Kingdom},
  author_keywords = {clustering; collaborative tagging; ontology learning; recommendation; user interest; user profiling},
  document_type   = {Conference Paper},
  doi             = {10.1145/2428736.2428804},
  journal         = {ACM International Conference Proceeding Series},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84873338116&doi=10.1145%2f2428736.2428804&partnerID=40&md5=7b20d190dfda750433e2c8523e2d3ed7},
}

@Article{vanleeuwen_puspitaningrum_2012,
  author        = {Van Leeuwen, M. and Puspitaningrum, D.},
  title         = {Improving tag recommendation using few associations},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2012},
  volume        = {7619 LNCS},
  pages         = {184-194},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging services allow users to freely assign tags to resources. As the large majority of users enters only very few tags, good tag recommendation can vastly improve the usability of tags for techniques such as searching, indexing, and clustering. Previous research has shown that accurate recommendation can be achieved by using conditional probabilities computed from tag associations. The main problem, however, is that enormous amounts of associations are needed for optimal recommendation. We argue and demonstrate that pattern selection techniques can improve tag recommendation by giving a very favourable balance between accuracy and computational demand. That is, few associations are chosen to act as information source for recommendation, providing high-quality recommendation and good scalability at the same time. We provide a proof-of-concept using an off-the-shelf pattern selection method based on the Minimum Description Length principle. Experiments on data from Delicious, LastFM and YouTube show that our proposed methodology works well: applying pattern selection gives a very favourable trade-off between runtime and recommendation quality. © Springer-Verlag Berlin Heidelberg 2012.},
  affiliation   = {Dept. of Information and Computing Sciences, Universiteit Utrecht, Netherlands; Dept. of Computer Science, KU Leuven, Belgium},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-642-34156-4_18},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84868032350&doi=10.1007%2f978-3-642-34156-4_18&partnerID=40&md5=9207f41d00f57a8bec31e43559703813},
}

@Conference{trabelsi_etal_2012,
  author          = {Trabelsi, C. and Moulahi, B. and Yahia, S.B.},
  title           = {HMM-CARe: Hidden Markov models for context-aware tag recommendation in folksonomies},
  year            = {2012},
  pages           = {957-961},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems allow users to manually annotate web resources with freely chosen keywords aka tags without any restriction to a certain vocabulary. The resulting collection of all these users annotations constitute the so-called folksonomy. Such systems typically provide simple tag recommendations skills to increase the number of tags assigned to resources. In this this paper, we propose a novel Hidden Markov Model (HMM) based approach, called HMM-CARE, for tags recommendation. Specifically, we extend the HMM to include user's tagging intents, formally represented as triadic concepts. Carried out experiments emphasize the relevance of our proposal and open many thriving issues. © 2012 ACM.},
  affiliation     = {Faculty of Sciences of Tunis, University Tunis El-Manar, Tunis, Tunisia},
  author_keywords = {folksonomies; Hidden Markov Models; tag recommendation; triadic concepts},
  document_type   = {Conference Paper},
  doi             = {10.1145/2245276.2245461},
  journal         = {Proceedings of the ACM Symposium on Applied Computing},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84863575351&doi=10.1145%2f2245276.2245461&partnerID=40&md5=c1941e7f351c3dba08f4ff967543524b},
}

@Article{laere_etal_2012,
  author        = {Olivier Van Laere and Steven Schockaert and Bart Dhoedt},
  title         = {Georeferencing Flickr photos using language models at different levels of granularity: An evidence based approach},
  journal       = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year          = {2012},
  volume        = {16},
  pages         = {17 - 31},
  issn          = {1570-8268},
  note          = {The Semantic Web Challenge 2011},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.websem.2012.05.005},
  keywords      = {Dempster–Shafer evidence theory, Language models, Georeferencing, Web 2.0, Geographic information retrieval},
  url           = {http://www.sciencedirect.com/science/article/pii/S1570826812000741},
}

@InProceedings{fang_etal_2012,
  author        = {L. Fang and L. Wang and M. Li and J. Zhao and Y. Zou and L. Shao},
  title         = {Towards Automatic Tagging for Web Services},
  booktitle     = {2012 IEEE 19th International Conference on Web Services},
  year          = {2012},
  pages         = {528-535},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Tagging technique is widely used to annotate objects in Web 2.0 applications. Tags can support web service understanding, categorizing and discovering, which are important tasks in a service-oriented software system. However, most of existing web services' tags are annotated manually. Manual tagging is time-consuming. In this paper, we propose a novel approach to tag web services automatically. Our approach consists of two tagging strategies, tag enriching and tag extraction. In the first strategy, we cluster web services using WSDL documents, and then we enrich tags for a service with the tags of other services in the same cluster. Considering our approach may not generate enough tags by tag enriching, we also extract tags from WSDL documents and related descriptions in the second step. To validate the effectiveness of our approach, a series of experiments are carried out based on web-scale web services. The experimental results show that our tagging method is effective, ensuring the number and quality of generated tags. We also show how to use tagging results to improve the performance of a web service search engine, which can prove that our work in this paper is useful and meaningful.},
  doi           = {10.1109/ICWS.2012.99},
  keywords      = {Web services;document handling;service-oriented architecture;WSDL documents;Web 2.0 applications;Web service clustering;automatic tagging;manual tagging;service-oriented software system;Blogs;Equations;Feature extraction;Mathematical model;Search engines;Tagging;Web services;Automatic;Cluster;Discovery;Tag;Web Services},
}

@InProceedings{kalender_dang_2012,
  author        = {M. Kalender and J. Dang},
  title         = {SKMT: A Semantic Knowledge Management Tool for Content Tagging, Search and Management},
  booktitle     = {2012 Eighth International Conference on Semantics, Knowledge and Grids},
  year          = {2012},
  pages         = {112-119},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {With an ever increasing amount of content, we heavily rely on search engine to locate documents. Manual content Tagging can improve search results by allowing search engines to exploit tags generated by content authors. Manual tagging method introduces issues of bias and inconsistency from ad-hoc tagging, and increase burden to the authors. In this paper, we first introduce a semantic tagging engine that automatically generates semantic tags for the given documents. This tagging engine provides the ground for realizing semantic search, based on meanings of search terms and content tags. Then we present a Semantic Knowledge Management Tool (SKMT) as a semantic search and knowledge management platform to search, analyze and manage enterprise content. SKMT can scan different content sources and generate indexes of semantic keywords. Its user-friendly interface allows users to manage various data sources, search, explore and visualize search results at semantic level. Higher precision of semantic search and semantic data visualization are also demonstrated with examples as benefits of SKMT.},
  doi           = {10.1109/SKG.2012.48},
  keywords      = {data visualisation;indexing;knowledge management;search engines;user interfaces;SKMT;content management;content search;content tagging;enterprise content;search engine;semantic data visualization;semantic keyword indexes;semantic knowledge management tool;semantic search;tagging engine;user-friendly interface;Encyclopedias;Internet;Ontologies;Search engines;Semantics;Tagging;Knowledge management;content indexing;semantic search},
}

@Article{olvera_godoy_2012,
  author        = {E. Portilla Olvera and D. Godoy},
  title         = {Evaluating Term Weighting Schemes for Content-based Tag Recommendation in Social Tagging Systems},
  journal       = {IEEE Latin America Transactions},
  year          = {2012},
  volume        = {10},
  number        = {4},
  pages         = {1973-1980},
  month         = {June},
  issn          = {1548-0992},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging systems allow users to publish different type of resources, such as Web pages or pictures, annotate them using keywords or tags and share their resources with other users. These systems achieved widespread success on the Web on account of the simplicity for organizing resources using open-ended tags. Recently, tag recommendation strategies have been proposed to alleviate the problems of ambiguity, syntactic variations and noise in tags cause by the inherent characteristics of natural language. In this work we proposed a content-based approach that generates a list of suggested tags for annotating a given resource starting from an analysis of its textual content exclusively. Thus, the proposed method can be used in situations in which there is not enough information for creating a tag-based user profile or compare the user with others. For extracting the more relevant words different term weighting approaches were evaluated, particularly considering the HTML structure of Web pages and the grammatical category of words in order to determine promising tag candidates. Experimental results of applying this technique to tag recommendation using several term weighting approaches are reported and compared.},
  doi           = {10.1109/TLA.2012.6272482},
  keywords      = {Web sites;grammars;hypermedia markup languages;natural language processing;text analysis;HTML structure;Web page;content-based approach;content-based tag recommendation;grammatical category;natural language;open-ended tag;resource annotation;social tagging system;syntactic variation;tag noise;tag-based user profile;term weighting scheme;textual content;word;Abstracts;HTML;Organizing;Syntactics;Tagging;Vectors;Web pages;Social tagging systems;content-based recommendation;text mining, rank1},
}

@InProceedings{wang_etal_2013,
  author        = {X. Wang and S. Li and X. Zou and R. Chen and B. Zhou},
  title         = {An Automatic Tag Recommendation Algorithm for Micro-blogging Users},
  booktitle     = {2013 International Conference on Computer Sciences and Applications},
  year          = {2013},
  pages         = {398-401},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Online social networks such as Sina Weibo micro-blogging website allow user to annotate himself (or herself) using tags, which describe the characteristics of the user. Many researches have been done on photos, films, commodities but rare researches on users. In this paper, we try to recommend tags for users who can communicate directly with other users. Our method is based on interactive relations between users and is low cost. We present and evaluate tag recommendation method to support the user self-annotation task by recommending a set of tags for users. Experiment evaluations on real and large Sina Weibo dataset which contains more than 140 million users and distributed processing framework Hadoop shows that our method can effectively recommend relevant tags to users.},
  doi           = {10.1109/CSA.2013.100},
  keywords      = {information retrieval;social networking (online);Sina Weibo microblogging Web site;automatic tag recommendation algorithm;distributed processing framework Hadoop;online social network;user self-annotation task;Collaboration;Computers;Equations;Mathematical model;Social network services;Tagging;Web sites;Micro-blogging;Social Network;Tag Recommendation;User Tags},
}

@Article{ivanov_etal_2013,
  author        = {I. Ivanov and P. Vajda and P. Korshunov and T. Ebrahimi},
  title         = {Comparative Study of Trust Modeling for Automatic Landmark Tagging},
  journal       = {IEEE Transactions on Information Forensics and Security},
  year          = {2013},
  volume        = {8},
  number        = {6},
  pages         = {911-923},
  month         = {June},
  issn          = {1556-6013},
  __markedentry = {[felipe:1]},
  abstract      = {Many images uploaded to social networks are related to travel, since people consider traveling to be an important event in their life. However, a significant amount of travel images on the Internet lack proper geographical annotations or tags. In many cases, the images are tagged manually. One way to make this time-consuming manual tagging process more efficient is to propagate tags from a small set of tagged images to the larger set of untagged images automatically. In this paper, we present a system for automatic geotag propagation in images based on the similarity between image content (famous landmarks) and its context (associated geotags). In such a scenario, however, an incorrect or a spam tag can damage the integrity and reliability of the automated propagation system. Therefore, for reliable geotags propagation, we suggest adopting a user trust model based on social feedback from the users of the photo-sharing system. We compare this socially-driven approach with other user trust models via experiments and subjective testing on an image database of various famous landmarks. Results demonstrate that relying on user feedback is more efficient, since the number of propagated tags more than doubles without loss of accuracy compared to using other models or propagating without trust modeling.},
  doi           = {10.1109/TIFS.2013.2242889},
  keywords      = {data privacy;social networking (online);trusted computing;user interfaces;visual databases;automatic geotag propagation;automatic landmark tagging;geographical annotation;geographical tag;image content;image database;photo-sharing system;social feedback;social network;spam tag;subjective testing;tag propagation;travel image;trust modeling;user feedback;user trust model;Context;Global Positioning System;Manuals;Reliability;Social network services;Tagging;Visualization;Geotags;object duplicate detection;social networks;social tagging;spam combatting;tag propagation;trust modeling},
}

@InProceedings{kakade_kakade_2013,
  author        = {S. R. Kakade and N. R. Kakade},
  title         = {A novel approach to link semantic gap between images and tags via probabilistic ranking},
  booktitle     = {2013 IEEE International Conference on Computational Intelligence and Computing Research},
  year          = {2013},
  pages         = {1-6},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Nowadays there is tremendous fame of social networking sites that leads to a broad investigation in tag-based social image search. Both visual features and tags plays important role in the research field. Existing approach uses tags and visual features consecutively or independently to estimate the relevance of images. In this paper we tackle the problems of Automatic Image Annotation (AIA) for image retrieval that delivers the global image search engine that covers different applications such as Image to Image Retrieval, Image to Tag suggestions, Tag to Image Retrieval and Tag to Tag suggestion which is based on user contributed tags on the websites such as flicker. The proposed approach comprises of global feature extractions of images. Then we predicate relationship between image and tag by using asymmetric graph as a probabilistic view. Once the relationship is found ranking is employed with the help of t-step random walk model. Finally pseudo relevance feedback is applied on the ranked images. Additionally experimental analysis of above model is conducted on Corel image dataset to illustrate the effectiveness of this approach.},
  doi           = {10.1109/ICCIC.2013.6724166},
  keywords      = {feature extraction;image retrieval;random processes;search engines;AIA;Corel image dataset;Flicker;Web sites;asymmetric graph;automatic image annotation;global feature extractions;global image search engine;image retrieval;link semantic gap;probabilistic ranking;pseudo relevance feedback;random walk model;social networking sites;tag suggestion;tag-based social image search;user contributed tags;visual features;Bipartite graph;Feature extraction;Image color analysis;Image edge detection;Image retrieval;Vectors;Visualization;Automatic Image Annotation;Content-based image retrieval;Image annotation;Random Walk;Text-based image retrieval},
}

@InProceedings{xia_etal_2013a,
  author        = {X. Xia and D. Lo and X. Wang and B. Zhou},
  title         = {Tag recommendation in software information sites},
  booktitle     = {2013 10th Working Conference on Mining Software Repositories (MSR)},
  year          = {2013},
  pages         = {287-296},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {Nowadays, software engineers use a variety of online media to search and become informed of new and interesting technologies, and to learn from and help one another. We refer to these kinds of online media which help software engineers improve their performance in software development, maintenance and test processes as software information sites. It is common to see tags in software information sites and many sites allow users to tag various objects with their own words. Users increasingly use tags to describe the most important features of their posted contents or projects. In this paper, we propose TagCombine, an automatic tag recommendation method which analyzes objects in software information sites. TagCombine has 3 different components: 1. multilabel ranking component which considers tag recommendation as a multi-label learning problem; 2. similarity based ranking component which recommends tags from similar objects; 3. tag-term based ranking component which considers the relationship between different terms and tags, and recommends tags after analyzing the terms in the objects. We evaluate TagCombine on 2 software information sites, StackOverflow and Freecode, which contain 47,668 and 39,231 text documents, respectively, and 437 and 243 tags, respectively. Experiment results show that for StackOverflow, our TagCombine achieves recall@5 and recall@10 scores of 0.5964 and 0.7239, respectively; For Freecode, it achieves recall@5 and recall@10 scores of 0.6391 and 0.7773, respectively. Moreover, averaging over StackOverflow and Freecode results, we improve TagRec proposed by Al-Kofahi et al. by 22.65% and 14.95%, and the tag recommendation method proposed by Zangerle et al. by 18.5% and 7.35% for recall@5 and recall@10 scores.},
  doi           = {10.1109/MSR.2013.6624040},
  issn          = {2160-1852},
  keywords      = {learning (artificial intelligence);program testing;recommender systems;social networking (online);software maintenance;text analysis;Freecode;StackOverflow;TagCombine;TagRec;automatic tag recommendation method;multilabel learning problem;multilabel ranking component;online media;similarity based ranking component;software development;software engineers;software information sites;software maintenance;software test processes;tag-term based ranking component;text documents;Educational institutions;Media;Prediction algorithms;Search problems;Software;Software algorithms;Vectors;Online Media;Software Information Sites;Tag Recommendation;TagCombine},
}

@InProceedings{li_etal_2013,
  author        = {S. Li and G. Huang and R. Tan and R. Pan},
  title         = {Tag-Weighted Dirichlet Allocation},
  booktitle     = {2013 IEEE 13th International Conference on Data Mining},
  year          = {2013},
  pages         = {438-447},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {In the past two decades, there has been a huge amount of document data with rich tag information during the evolution of the Internet, which can be called semi-structured data. These semi-structured data contain both unstructured features (e.g., plain text) and metadata, such as tags in html files or author and venue information in research articles. It's of great interest to model such kind of data. Most previous works focused on modeling the unstructured data. Some other methods have been proposed to model the unstructured data with specific tags. To build a general model for semi-structured documents remains an important problem in terms of both model fitness and efficiency. In this paper, we propose a novel method to model the tagged documents by a so-called Tag-Weighted Dirichlet Allocation (TWDA). TWDA is a framework that leverages both the tags and words in each document to infer the topic components for the documents. This allows not only to learn the document-topic and topic-word distributions, but also to infer the tag-topic distributions for text mining (e.g., classification, clustering, and recommendations). Moreover, TWDA can automatically infer the probabilistic weights of tags for each document, that can be used to predict the tags in one document. We present an efficient variational inference method with an EM algorithm for estimating the model parameters. The experimental results show the effectiveness, efficiency and robustness of our TWDA approach by comparing it with the state-of-the-art methods on four corpora in document modeling, tags prediction and text classification.},
  doi           = {10.1109/ICDM.2013.11},
  issn          = {1550-4786},
  keywords      = {data mining;expectation-maximisation algorithm;inference mechanisms;parameter estimation;pattern classification;pattern clustering;probability;recommender systems;text analysis;EM algorithm;HTML files;TWDA framework;author information;document tag leveraging;document word leveraging;document-topic distribution learning;metadata;model efficiency;model fitness;model parameter estimation;plain text;probabilistic weights;research articles;semistructured document data;tag information;tag prediction;tag-topic distribution inference;tag-weighted Dirichlet allocation;tagged document modelling;text classification;text clustering;text mining;text recommendations;topic components;topic-word distribution learning;variational inference method;venue information;Analytical models;Data models;Mathematical model;Predictive models;Probabilistic logic;Resource management;Vectors;Dirichlet allocation;Tag-Weighted;topic model;variational inference, rank1},
}

@InProceedings{liu_etal_2013,
  author        = {S. Liu and Y. Zhu and J. Guo and Y. Wang and X. Cheng and Y. Liu},
  title         = {A Blending Method for Automated Social Tagging},
  booktitle     = {2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year          = {2013},
  volume        = {1},
  pages         = {115-120},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging has grown in popularity on the web due to its effectiveness in organizing and accessing web pages. This short paper addresses the problem of automated social tagging, which aims to predict tags for web pages automatically and help with future navigation, filtering or search. We explore and find three foundations of the collaborative tags in social tagging services, that are consistency, sharability and stability. The complementary advantages are studied among three well-known methods, i.e. TF-weighted keyword extraction, collaborative filtering approach, and Corr-LDA (correspondence latent Dirichlet allocation) topic model. We then propose a blending model for automated social tagging to emphasize all the foundations, which linearly combines those tags generated by the three methods, and a permutation probability model is built to learn the linear blending. With the experiments on 50,000 training and 10,000 testing web pages from Delicious database, the results show that our blending method outperforms the four baselines. Furthermore, compared with both topic models, Corr-LDA and mixed membership LDA, our approach results in 14.2% and 25.6% of NDCG10 improvement separately.},
  doi           = {10.1109/WI-IAT.2013.17},
  keywords      = {Internet;collaborative filtering;data integrity;social networking (online);Corr-LDA topic model;Del.icio.us database;TF-weighted keyword extraction;automated social tagging services;blending model;collaborative filtering approach;collaborative tags;consistency;correspondence latent Dirichlet allocation topic model;linear blending;permutation probability model;sharability;stability;testing Web pages;training Web pages;Accuracy;Collaboration;Data models;Equations;Mathematical model;Stability analysis;Tagging;automatic annotation;collaborative filtering;social tagging;topic model, rank1},
}

@Article{hsu_2013,
  author        = {I-Ching Hsu},
  title         = {Integrating ontology technology with folksonomies for personalized social tag recommendation},
  journal       = {Applied Soft Computing},
  year          = {2013},
  volume        = {13},
  number        = {8},
  pages         = {3745 - 3750},
  issn          = {1568-4946},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.asoc.2013.03.004},
  keywords      = {Recommendation system, Social tag, Ontology, Semantic Web, Folksonomy},
  url           = {http://www.sciencedirect.com/science/article/pii/S1568494613001087},
}

@Article{li_etal_2013a,
  author        = {Zechao Li and Jing Liu and Hanqing Lu},
  title         = {Nonlinear matrix factorization with unified embedding for social tag relevance learning},
  journal       = {Neurocomputing},
  year          = {2013},
  volume        = {105},
  pages         = {38 - 44},
  issn          = {0925-2312},
  note          = {Learning for Scalable Multimedia Representation},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2012.02.046},
  keywords      = {Matrix factorization, Unified embedding, Tag relevance, Social image retrieval},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231212006984},
}

@Article{deng_etal_2013,
  author        = {Zhi-Hong Deng and Zhong-Hui Wang and Jie Zhang},
  title         = {ROBIN: A novel personal recommendation model based on information propagation},
  journal       = {Expert Systems with Applications},
  year          = {2013},
  volume        = {40},
  number        = {13},
  pages         = {5306 - 5313},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2013.03.039},
  keywords      = {Data analysis, Data mining, Recommendation system, Information propagation, Performance evaluation},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417413002261},
}

@Article{zhang_etal_2013,
  author        = {Xiaoming Zhang and Xiaojian Zhao and Zhoujun Li and Jiali Xia and Ramesh Jain and Wenhan Chao},
  title         = {Social image tagging using graph-based reinforcement on multi-type interrelated objects},
  journal       = {Signal Processing},
  year          = {2013},
  volume        = {93},
  number        = {8},
  pages         = {2178 - 2189},
  issn          = {0165-1684},
  note          = {Indexing of Large-Scale Multimedia Signals},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.sigpro.2012.05.021},
  keywords      = {Social image tagging, Image retrieval, Reinforcement, Tag ranking, Personal tagging},
  url           = {http://www.sciencedirect.com/science/article/pii/S0165168412001661},
}

@Conference{chelmis_prasanna_2013,
  author          = {Chelmis, C. and Prasanna, V.K.},
  title           = {Exploring generative models of tripartite graphs for recommendation in social media},
  year            = {2013},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {As social media sites grow in popularity, tagging has naturally emerged as a method of searching, categorizing and filtering online information, especially multimedia content. The unrestricted vocabulary users choose from to annotate content however, has often lead to an explosion of the size of space in which search is performed. This paper is concerned with investigating generative models of social annotations, and testing their efficiency with respect to two information consumption oriented tasks. One task considers recommending new tags (similarly new resources) for new, previously unknown users. We use perplexity as a standard measure for estimating the generalization performance of a probabilistic model. The second task is aimed at recommending new users to connect with. In this task, we examine which users' activity is most discriminative in predicting social ties: annotation (i.e. tags), resource usage (i.e. artists), or collective annotation of resources altogether. For the second task, we propose a framework to integrate the modeling of social annotations with network proximity. The proposed approach consists of two steps: (1) discovering salient topics that characterize users, resources and annotations; and (2) enhancing the recommendation power of such models by incorporating social clues from the immediate neighborhood of users. In particular, we propose four classification schemes for social link recommendation, which we evaluate on a real-world dataset from Last.fm. Our results demonstrate significant improvements over traditional approaches. Copyright 2013 ACM.},
  affiliation     = {Department of Computer Science, University of Southern California, Los Angeles, CA, United States; Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States},
  art_number      = {2},
  author_keywords = {Collaborative tagging; Graphical models; Link recommendation; Social media; Unsupervised learning; User modeling},
  document_type   = {Conference Paper},
  doi             = {10.1145/2463656.2463658},
  journal         = {Proceedings of the 4th International Workshop on Modeling Social Media: Mining, Modeling and Recommending 'Things' in Social Media, MSM 2013},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2463656.2463658},
}

@Article{font_etal_2013,
  author          = {Font, F. and Serrà, J. and Serra, X.},
  title           = {Folksonomy-based tag recommendation for collaborative tagging systems},
  journal         = {International Journal on Semantic Web and Information Systems},
  year            = {2013},
  volume          = {9},
  number          = {2},
  pages           = {1-30},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging has emerged as a common solution for labelling and organising online digital content. However, collaborative tagging systems typically suffer from a number of issues such as tag scarcity or ambiguous labelling. As a result, the organisation and browsing of tagged content is far from being optimal. In this work the authors present a general scheme for building a folksonomy-based tag recommendation system to help users tagging online content resources. Based on this general scheme, the authorse describe eight tag recommendation methods and extensively evaluate them with data coming from two real-world large-scale datasets of tagged images and sound clips. Their results show that the proposed methods can effectively recommend relevant tags, given a set of input tags and tag co-occurrence information. Moreover, the authors show how novel strategies for selecting the appropriate number of tags to be recommended can significantly improve methods performances. Approaches such as the one presented here can be useful to obtain more comprehensive and coherent descriptions of tagged resources, thus allowing a better organisation, browsing and reuse of online content. Moreover, they can increase the value of folksonomies as reliable sources for knowledge-mining. Copyright © 2013, IGI Global.},
  affiliation     = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Artificial Intelligence Research Institute (IIIA-CSIC), Spanish National Research Council, Bellaterra, Spain},
  author_keywords = {Collaborative Tagging; Folksonomies; Intelligent System; Online Digital Content; Recommendation System},
  document_type   = {Article},
  doi             = {10.4018/jswis.2013040101},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.igi-global.com/gateway/article/94597},
}

@Conference{yin_etal_2013,
  author          = {Yin, D. and Guo, S. and Chidlovskii, B. and Davison, B.D. and Archambeau, C. and Bouchard, G.},
  title           = {Connecting comments and tags: Improved modeling of social tagging systems},
  year            = {2013},
  pages           = {547-556},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems are now deployed extensively to help users share and organize resources. Tag prediction and recommendation can simplify and streamline the user experience, and by modeling user preferences, predictive accuracy can be significantly improved. However, previous methods typically model user behavior based only on a log of prior tags, neglecting other behaviors and information in social tagging systems, e.g., commenting on items and connecting with other users. On the other hand, little is known about the connection and correlations among these behaviors and contexts in social tagging systems. In this paper, we investigate improved modeling for predictive social tagging systems. Our explanatory analyses demonstrate three significant challenges: coupled high order interaction, data sparsity and cold start on items. We tackle these problems by using a generalized latent factor model and fully Bayesian treatment. To evaluate performance, we test on two real-world data sets from Flickr and Bibsonomy. Our experiments on these data sets show that to achieve best predictive performance, it is necessary to employ a fully Bayesian treatment in modeling high order relations in social tagging system. Our methods noticeably outperform state-of-the-art approaches. © 2013 ACM.},
  affiliation     = {Dept. of Computer Science and Engineering, Lehigh University, Bethlehem, PA, United States; Xerox Research Center Europe, Grenoble, France},
  author_keywords = {co-factorization; personalized comment recommendation; personalized tag prediction; social tagging; tag recommendation},
  document_type   = {Conference Paper},
  doi             = {10.1145/2433396.2433466},
  journal         = {WSDM 2013 - Proceedings of the 6th ACM International Conference on Web Search and Data Mining},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2433396.2433466},
}

@Article{lops_etal_2013,
  author          = {Lops, P. and De Gemmis, M. and Semeraro, G. and Musto, C. and Narducci, F.},
  title           = {Content-based and collaborative techniques for tag recommendation: An empirical evaluation},
  journal         = {Journal of Intelligent Information Systems},
  year            = {2013},
  volume          = {40},
  number          = {1},
  pages           = {41-61},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The rapid growth of the so-called Web 2.0 has changed the surfers' behavior. A new democratic vision emerged, in which users can actively contribute to the evolution of the Web by producing new content or enriching the existing one with user generated metadata. In this context the use of tags, keywords freely chosen by users for describing and organizing resources, spread as a model for browsing and retrieving web contents. The success of that collaborative model is justified by two factors: firstly, information is organized in a way that closely reflects the users' mental model; secondly, the absence of a controlled vocabulary reduces the users' learning curve and allows the use of evolving vocabularies. Since tags are handled in a purely syntactical way, annotations provided by users generate a very sparse and noisy tag space that limits the effectiveness for complex tasks. Consequently, tag recommenders, with their ability of providing users with the most suitable tags for the resources to be annotated, recently emerged as a way of speeding up the process of tag convergence. The contribution of this work is a tag recommender system implementing both a collaborative and a content-based recommendation technique. The former exploits the user and community tagging behavior for producing recommendations, while the latter exploits some heuristics to extract tags directly from the textual content of resources. Results of experiments carried out on a dataset gathered from Bibsonomy show that hybrid recommendation strategies can outperform single ones and the way of combining them matters for obtaining more accurate results. © 2012 Springer Science+Business Media, LLC.},
  affiliation     = {Department of Computer Science, University of Bari Aldo Moro, Via E. Orabona, 4, I70126 Bari, Italy},
  author_keywords = {Collaborative tagging; Folksonomies; Recommender systems; Tag recommendation; Web 2.0},
  document_type   = {Article},
  doi             = {10.1007/s10844-012-0215-6},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-1066/Paper10.pdf},
}

@Conference{gueye_etal_2013,
  author          = {Gueye, M. and Abdessalem, T. and Naacke, H.},
  title           = {FoldCons: A simple way to improve tag recommendation},
  year            = {2013},
  volume          = {1066},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend tags to a user for tagging an item. In this paper we present a part of our work in progress which is a novel improvement of recommendations by re-ranking the output of a tag recommender. We mine association rules between candidates tags in order to determine a more consistent list of tags to recommend. Our method is an add-on one which leads to better recommendations as we show in this paper. It is easily parallelizable and morever it may be applied to a lot of tag recommenders. The experiments we did on five datasets with two kinds of tag recommender demonstrated the efficiency of our method.},
  affiliation     = {Université Cheikh Anta DIOP, Dakar, Senegal; Institut Telecom, Telecom ParisTech, Paris, France; LIP6, UPMC Sorbonne Universités - Paris 6, Paris, France},
  author_keywords = {Association rules mining; Factorization models; Social network; Tag recommendation},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84924370976&partnerID=40&md5=fabd13a20f0884428c4e6e52bcc366b0},
}

@Article{laere_etal_2013,
  author        = {Olivier Van Laere and Steven Schockaert and Bart Dhoedt},
  title         = {Georeferencing Flickr resources based on textual meta-data},
  journal       = {Information Sciences},
  year          = {2013},
  volume        = {238},
  pages         = {52 - 74},
  issn          = {0020-0255},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.ins.2013.02.045},
  keywords      = {Text mining, Metadata, Geographic information retrieval, Classification, Semi-structured data},
  url           = {http://www.sciencedirect.com/science/article/pii/S002002551300162X},
}

@Article{peng_etal_2013,
  author        = {Jinye Peng and Yi Shen and Jianping Fan},
  title         = {Cross-modal social image clustering and tag cleansing},
  journal       = {Journal of Visual Communication and Image Representation},
  year          = {2013},
  volume        = {24},
  number        = {7},
  pages         = {895 - 910},
  issn          = {1047-3203},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.jvcir.2013.06.004},
  keywords      = {Cross-modal image clustering, Social tag cleansing, Weakly-tagged social images, K-way min–max cut, Mixture-of-kernels, Spectral clustering, Spam tag detection, Image similarity measurement},
  url           = {https://doi.org/10.5220/0004439904670474},
}

@Conference{djuana_etal_2013,
  author          = {Djuana, E. and Xu, Y. and Li, Y. and Josang, A. and Cox, C.},
  title           = {An ontology-based method for sparsity problem in tag recommendation},
  year            = {2013},
  volume          = {2},
  pages           = {467-474},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Tags or personal metadata for annotating web resources have been widely adopted in Web 2.0 sites. However, as tags are freely chosen by users, the vocabularies are diverse, ambiguous and sometimes only meaningful to individuals. Tag recommenders may assist users during tagging process. Its objective is to suggest relevant tags to use as well as to help consolidating vocabulary in the systems. In this paper we discuss our approach for providing personalized tag recommendation by making use of existing domain ontology generated from folksonomy. Specifically we evaluated the approach in sparse situation. The evaluation shows that the proposed ontology-based method has improved the accuracy of tag recommendation in this situation.},
  affiliation     = {School of Electrical Engineering and Computer Science, Queensland University of Technology, Brisbane, Australia; Department of Informatics, University of Oslo, Oslo, Norway; Rummble Ltd, London, United Kingdom},
  author_keywords = {Collaborative tagging; Domain ontology; Folksonomy; Sparsity problem; Tag recommendation},
  document_type   = {Conference Paper},
  journal         = {ICEIS 2013 - Proceedings of the 15th International Conference on Enterprise Information Systems},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84887730818&partnerID=40&md5=53d2228d4a98b843018138b56eee9b9c},
}

@InProceedings{gueye_etal_2014,
  author        = {M. Gueye and T. Abdessalem and H. Nacke},
  title         = {A Social and Popularity-Based Tag Recommender},
  booktitle     = {2014 IEEE Fourth International Conference on Big Data and Cloud Computing},
  year          = {2014},
  pages         = {318-325},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommendation aims to recommend to a user the most suited tags for a given item. It is an important functionality of resource sharing systems. In this paper we propose a recommendation algorithm, called Fas Tag, that links the relevance of the tags to both their popularity and the opinions of the user's neighbors. Fas Tag assumes that the users are organized in a weighted graph representing, for instance, the similarity or the trust between them. The two salient aspects of Fas Tag are the scoring function it uses to evaluate the relevance of the tags, and its low computation cost. Thus, Fas Tag can make online recommendations, even for large datasets. Moreover, we improve the accuracy of its recommendations by adjusting automatically the size of the recommended list of tags. The experiments we did on several datasets show a significant improvement of the accuracy of the recommendations.},
  doi           = {10.1109/BDCloud.2014.44},
  keywords      = {graph theory;recommender systems;social networking (online);Fas Tag;online recommendations;scoring function;social popularity-based tag recommender;weighted graph;Accuracy;Equations;Mathematical model;Navigation;Scalability;Social network services;Tagging;Accuracy;Scalability;Social network;Tag recommendation},
}

@InProceedings{hong_etal_2014,
  author        = {H. K. Hong and K. W. Park and D. H. Lee},
  title         = {Tag recommendation system for multimedia retrieval in mobile Environment},
  booktitle     = {The 18th IEEE International Symposium on Consumer Electronics (ISCE 2014)},
  year          = {2014},
  pages         = {1-2},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Nowadays, due to the explosive growth of mobile devices, social networks and multimedia contents sharing systems have become increasingly popular. However, the characteristics of mobile devices and tags make it difficult to perform an accurate tag-based multimedia retrieval. In this paper, we propose a novel tag recommendation system for multimedia retrieval based on the user trust model which takes advantage of tag clusters of Flickr and visual similarity of multimedia. The proposed system makes it possible to provide the reliable and relevant tags to user in mobile environment.},
  doi           = {10.1109/ISCE.2014.6884307},
  issn          = {0747-668X},
  keywords      = {information retrieval;mobile computing;multimedia computing;recommender systems;social networking (online);Flickr;mobile devices;mobile environment;multimedia content sharing systems;multimedia retrieval;social networks;tag clusters;tag recommendation system;tag-based multimedia retrieval;user trust model;visual similarity;Mobile communication;Mobile handsets;Multimedia communication;Reliability;Semantics;Visualization;Social Multimedia Tagging;Social Tag;Tag Recommendation;User Trust Model},
}

@InProceedings{feng_wang_2014,
  author        = {W. Feng and J. Wang},
  title         = {We can learn your #hashtags: Connecting tweets to explicit topics},
  booktitle     = {2014 IEEE 30th International Conference on Data Engineering},
  year          = {2014},
  pages         = {856-867},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {In Twitter, users can annotate tweets with hashtags to indicate the ongoing topics. Hashtags provide users a convenient way to categorize tweets. From the system's perspective, hashtags play an important role in tweet retrieval, event detection, topic tracking, and advertising, etc. Annotating tweets with the right hashtags can lead to a better user experience. However, two problems remain unsolved during an annotation: (1) Before the user decides to create a new hashtag, is there any way to help her/him find out whether some related hashtags have already been created and widely used? (2) Different users may have different preferences for categorizing tweets. However, few work has been done to study the personalization issue in hashtag recommendation. To address the above problems, we propose a statistical model for personalized hashtag recommendation in this paper. With millions of <;tweet, hashtag> pairs being published everyday, we are able to learn the complex mappings from tweets to hashtags with the wisdom of the crowd. Two questions are answered in the model: (1) Different from traditional item recommendation data, users and tweets in Twitter have rich auxiliary information like URLs, mentions, locations, social relations, etc. How can we incorporate these features for hashtag recommendation? (2) Different hashtags have different temporal characteristics. Hashtags related to breaking events in the physical world have strong rise-and-fall temporal pattern while some other hashtags remain stable in the system. How can we incorporate hashtag related features to serve for hashtag recommendation? With all the above factors considered, we show that our model successfully outperforms existing methods on real datasets crawled from Twitter.},
  doi           = {10.1109/ICDE.2014.6816706},
  issn          = {1063-6382},
  keywords      = {social networking (online);#;Twitter;advertising;complex mappings;event detection;explicit topics;hashtags;personalization issue;personalized hashtag recommendation;rise-and-fall temporal pattern;statistical model;temporal characteristics;topic tracking;tweet annotation;tweet categorization;tweet retrieval;tweets;Advertising;Data models;History;Joining processes;Training;Twitter;Web pages},
}

@Article{liu_etal_2014,
  author        = {J. Liu and Z. Li and J. Tang and Y. Jiang and H. Lu},
  title         = {Personalized Geo-Specific Tag Recommendation for Photos on Social Websites},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2014},
  volume        = {16},
  number        = {3},
  pages         = {588-600},
  month         = {April},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging becomes increasingly important to organize and search large-scale community-contributed photos on social websites. To facilitate generating high-quality social tags, tag recommendation by automatically assigning relevant tags to photos draws particular research interest. In this paper, we focus on the personalized tag recommendation task and try to identify user-preferred, geo-location-specific as well as semantically relevant tags for a photo by leveraging rich contexts of the freely available community-contributed photos. For users and geo-locations, we assume they have different preferred tags assigned to a photo, and propose a subspace learning method to individually uncover the both types of preferences. The goal of our work is to learn a unified subspace shared by the visual and textual domains to make visual features and textual information of photos comparable. Considering the visual feature is a lower level representation on semantics than the textual information, we adopt a progressive learning strategy by additionally introducing an intermediate subspace for the visual domain, and expect it to have consistent local structure with the textual space. Accordingly, the unified subspace is mapped from the intermediate subspace and the textual space respectively. We formulate the above learning problems into a united form, and present an iterative optimization with its convergence proof. Given an untagged photo with its geo-location to a user, the user-preferred and the geo-location-specific tags are found by the nearest neighbor search in the corresponding unified spaces. Then we combine the obtained tags and the visual appearance of the photo to discover the semantically and visually related photos, among which the most frequent tags are used as the recommended tags. Experiments on a large-scale data set collected from Flickr verify the effectivity of the proposed solution.},
  doi           = {10.1109/TMM.2014.2302732},
  keywords      = {classification;convergence of numerical methods;information retrieval;iterative methods;learning (artificial intelligence);optimisation;recommender systems;social networking (online);Flickr;convergence proof;geo-location-specific identification;high-quality social tag generation;iterative optimization;large-scale community-contributed photo organization;large-scale community-contributed photo searching;large-scale data set;nearest neighbor search;personalized geo-specific tag recommendation;personalized tag recommendation task;progressive learning strategy;research interest;social Web sites;social tagging;subspace learning method;textual information;visual features;Context;History;Linear programming;Optimization;Semantics;Tagging;Visualization;Geo-location preference;personalized tag recommendation;subspace learning;tagging history;user preference},
}

@Article{lo_etal_2014,
  author        = {H. Y. Lo and S. D. Lin and H. M. Wang},
  title         = {Generalized k-Labelsets Ensemble for Multi-Label and Cost-Sensitive Classification},
  journal       = {IEEE Transactions on Knowledge and Data Engineering},
  year          = {2014},
  volume        = {26},
  number        = {7},
  pages         = {1679-1691},
  month         = {July},
  issn          = {1041-4347},
  __markedentry = {[felipe:1]},
  abstract      = {Label powerset (LP) method is one category of multi-label learning algorithm. This paper presents a basis expansions model for multi-label classification, where a basis function is an LP classifier trained on a random k-labelset. The expansion coefficients are learned to minimize the global error between the prediction and the ground truth. We derive an analytic solution to learn the coefficients efficiently. We further extend this model to handle the cost-sensitive multi-label classification problem, and apply it in social tagging to handle the issue of the noisy training set by treating the tag counts as the misclassification costs. We have conducted experiments on several benchmark datasets and compared our method with other state-of-the-art multi-label learning methods. Experimental results on both multi-label classification and cost-sensitive social tagging demonstrate that our method has better performance than other methods.},
  doi           = {10.1109/TKDE.2013.112},
  keywords      = {learning (artificial intelligence);minimisation;pattern classification;random processes;social networking (online);LP classifier;cost sensitive multilabel classification problem;expansion coefficients;generalized k-labelsets ensemble;global error minimization;label powerset method;misclassification cost;multilabel learning algorithm;noisy training set handling;random k-labelset;social tagging;tag counts;Laplace equations;Learning systems;Linear programming;Optimization;Prediction algorithms;Tagging;Training;Multi-label classification;cost-sensitive learning;ensemble method;hypergraph;labelset;multi-label classification;social tag;tag count, rank2},
}

@InProceedings{lei_liu_2014,
  author        = {Chenyi Lei and D. Liu},
  title         = {Image annotation via social diffusion analysis with common interests},
  booktitle     = {2014 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
  year          = {2014},
  pages         = {1-6},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic annotation of images is of crucial importance in image retrieval and management systems. Most of the existing annotation methods rely on content-based approach to annotation, whose effectiveness is restricted due to the semantic gap between low-level features and semantic annotations, as well as the irrelevance between annotations and image content. Recently, social media analysis has been investigated for image annotation. Inspired by the abundant social diffusion records of images in online social networks, we propose a novel image annotation approach based on social diffusion analysis. We present a common-interest model to interpret social diffusion, i.e. different images have different social diffusion routes due to the preferences of users, and such preferences are represented as common interests of pairwise users rather than personalized interests. We propose an image annotation framework that consists of learning of common interests, feature extraction from social diffusion records, and automatic annotation by learning to rank. Experimental results on a real-world dataset show that our proposed approach outperforms content-based and user-preference-based annotation methods.},
  doi           = {10.1109/ICMEW.2014.6890535},
  issn          = {1945-7871},
  keywords      = {feature extraction;image retrieval;social networking (online);automatic annotation;common interests;common-interest model;feature extraction;image annotation framework;image management system;image retrieval system;online social networks;semantic annotations;semantic gap;social diffusion analysis;social diffusion records;social media analysis;Abstracts;Positron emission tomography;Vectors;Common interests;image annotation;learning to rank;social diffusion analysis;social media},
}

@InProceedings{singhal_srivastava_2014,
  author        = {A. Singhal and J. Srivastava},
  title         = {Leveraging the web for automating tag expansion for low-content items},
  booktitle     = {Proceedings of the 2014 IEEE 15th International Conference on Information Reuse and Integration (IEEE IRI 2014)},
  year          = {2014},
  pages         = {545-552},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Tags, as high quality semantic descriptors, are used in categorization, clustering and efficient retrieval of various items in the web corpus. Images, videos, songs and similar multimedia items are the most common items which are tagged either manually or in a semiautomatic manner. However, the tagging process becomes complicated when the content structure of an item is not interpretable. Such a problems occurs in items like scientific research datasets or documents with very little text content. In this work, we propose a generalized approach to automate tag expansion for such low-content items. We leverage intelligence of the web to generate secondary content for such items for the tag expansion process. While automating tag expansion, we also address the problem of topic drift by automating removal of the noisy tags from the set of candidate new tags. The effectiveness of the proposed approach is tested on a real world dataset. The performance of the proposed is compared with Wikipedia based nearest neighbor tagging (WikiSem) and non-negative matrix factorization (NMF) based tag expansion approaches. Based on the Mean Reciprocal Rank (MRR) metric, the proposed approach was twice as accurate as the WikiSem baseline (0.27 vs 0.13) and at least 2.25 times the NMF baselines (0.27 vs 0.12).},
  doi           = {10.1109/IRI.2014.7051937},
  keywords      = {Internet;Web sites;information retrieval;text analysis;MRR metric;NMF based tag expansion approach;Web corpus;WikiSem;Wikipedia based nearest neighbor tagging;high quality semantic descriptors;item content structure;low-content items;mean reciprocal rank metric;multimedia items;nonnegative matrix factorization;tag expansion automation process;text content;topic drift problem;Databases;Google;Noise measurement;Search engines;Semantics;Tagging},
}

@Article{xu_etal_2014,
  author        = {Z. Xu and Y. Zhang and L. Cao},
  title         = {Social Image Analysis From a Non-IID Perspective},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2014},
  volume        = {16},
  number        = {7},
  pages         = {1986-1998},
  month         = {Nov},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {An image in social media, termed a social image, exhibits characteristics different from images widely discussed in image processing. They can be described by both content and social related attributes, called social image attributes, including visual contents, users, tags, and timestamps. There are strong coupling relationships between social image attributes, which make social images not independent and identically distributed (non-IID). By analyzing the relationships among these attributes, we can better understand the semantic activities conducted on such non-IID social images, hence enabling new applications including content organization, recommendation, and social activity understanding. In this article, we present a novel algorithm to analyze the coupling relationships between social images, which involves not only intra-coupled similarity within a social image attribute, but also inter-coupled similarity between attributes, in analyzing the non-IIDness of the similarity between social images. In particular, we propose a multi-entry version of the coupled similarity metric to deal with attributes (i.e., tags) which have a many-to-one relationship with respect to images. Experimental results on a Flickr group dataset show that the proposed algorithm captures coupling relationships and therefore achieves promising results in various applications, including image clustering and tagging.},
  doi           = {10.1109/TMM.2014.2342658},
  keywords      = {data mining;image processing;learning (artificial intelligence);pattern clustering;social networking (online);Flickr group dataset;Internet;content organization;content related attributes;image clustering;intercoupled similarity;intracoupled similarity;non IID perspective;not independent-and-identically distributed learning;social activity understanding;social image analysis;social image attributes;social media;social related attributes;structure mining;timestamps;visual contents;Algorithm design and analysis;Clustering algorithms;Couplings;Image analysis;Measurement;Multimedia communication;Visualization;Not independent and identically distributed (non-IID) learning;similarity metric;social media;structure mining, rank1},
}

@InProceedings{pliakos_kotropoulos_2014,
  author        = {K. Pliakos and C. Kotropoulos},
  title         = {Personalized music tagging using ranking on hypergraphs},
  booktitle     = {2014 6th International Symposium on Communications, Control and Signal Processing (ISCCSP)},
  year          = {2014},
  pages         = {628-631},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging enables users of social media sharing platforms to annotate multimedia items by employing arbitrary keywords (i.e., tags), which describe better the multimedia content. Several applications, such as personalized multimedia recommendation or music genre classification, to name a few, benefit from tagging. Clearly, tagging aims at bridging the semantic gap between human concepts and content retrieval exploiting low-level features extracted from the multimedia. Here, the problem of personalized tag recommendation is addressed in a “query and ranking” manner on hypergraphs. This way, the relationships between the different object types, such as user friendships, user groups, music tracks and tags are captured and tags are recommended for a certain track to a user. Ranking on hypergraphs is studied by enforcing either ℓ2 norm regularization or group sparsity. Experiments on a dataset collected from Last.fm demonstrate a promising tag recommendation accuracy.},
  doi           = {10.1109/ISCCSP.2014.6877953},
  keywords      = {Internet;graph theory;multimedia systems;music;query processing;recommender systems;social networking (online);arbitrary keywords;hypergraph query;hypergraph ranking;multimedia item annotation;personalized music tagging;personalized tag recommendation;social media sharing platforms;social tagging;Feature extraction;Media;Multimedia communication;Optimization;Tagging;Tensile stress;Vectors;Group Sparse Optimization;Hypergraph;Music Signal Processing;Tagging},
}

@InProceedings{sattigeri_etal_2014,
  author        = {P. Sattigeri and J. J. Thiagarajan and M. Shah and K. N. Ramamurthy and A. Spanias},
  title         = {A scalable feature learning and tag prediction framework for natural environment sounds},
  booktitle     = {2014 48th Asilomar Conference on Signals, Systems and Computers},
  year          = {2014},
  pages         = {1779-1783},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Building feature extraction approaches that can effectively characterize natural environment sounds is challenging due to the dynamic nature. In this paper, we develop a framework for feature extraction and obtaining semantic inferences from such data. In particular, we propose a new pooling strategy for deep architectures, that can preserve the temporal dynamics in the resulting representation. By constructing an ensemble of semantic embeddings, we employ an l1-reconstruction based prediction algorithm for estimating the relevant tags. We evaluate our approach on challenging environmental sound recognition datasets, and show that the proposed features outperform traditional spectral features.},
  doi           = {10.1109/ACSSC.2014.7094773},
  keywords      = {acoustic signal processing;feature extraction;learning (artificial intelligence);Iι-reconstruction based prediction algorithm;environmental sound recognition;feature extraction approach;scalable feature learning;semantic inferences;tag prediction framework;Computational modeling;Computer architecture;Correlation;Dictionaries;Feature extraction;Predictive models;Semantics, rank1},
}

@InProceedings{tomar_etal_2014,
  author        = {A. Tomar and F. Godin and B. Vandersmissen and W. D. Neve and R. V. d. Walle},
  title         = {Towards Twitter hashtag recommendation using distributed word representations and a deep feed forward neural network},
  booktitle     = {2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI)},
  year          = {2014},
  pages         = {362-368},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Hashtags are useful for categorizing and discovering content and conversations in online social networks. However, assigning hashtags requires additional user effort, hampering their widespread adoption. Therefore, in this paper, we introduce a novel approach for hashtag recommendation, targeting English language tweets on Twitter. First, we make use of a skip-gram model to learn distributed word representations (word2vec). Next, we make use of the distributed word representations learned to train a deep feed forward neural network. We test our deep neural network by recommending hashtags for tweets with user-assigned hashtags, using Mean Squared Error (MSE) as the objective function. We also test our deep neural network by recommending hashtags for tweets without user-assigned hashtags. Our experimental results show that the proposed approach recommends hashtags that are specific to the semantics of the tweets and that preserve the linguistic regularity of the tweets. In addition, our experimental results show that the proposed approach is capable of generating hashtags that have not been seen before.},
  doi           = {10.1109/ICACCI.2014.6968557},
  keywords      = {feedforward neural nets;mean square error methods;recommender systems;social networking (online);word processing;English language tweets;MSE;Twitter Hashtag recommendation;deep feed forward neural network;distributed word representations;linguistic regularity;mean squared error;online social networks;skip-gram model;user effort;user-assigned hashtags;Artificial neural networks;Dictionaries;Semantics;Testing;Vectors;Rectified Linear Units;Twitter;deep neural networks;distributed word representations;hashtag recommendation;word2vec, rank2},
}

@InProceedings{kapitsaki_2014,
  author        = {G. M. Kapitsaki},
  title         = {Annotating Web Service Sections with Combined Classification},
  booktitle     = {2014 IEEE International Conference on Web Services},
  year          = {2014},
  pages         = {622-629},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Web services are considered reusable elements that can be exploited stand-alone or as part of wider, generic or domain-specific, applications. With a vast variety of Web Services being offered by service providers, annotation techniques that add tags to services automatically are frequently exploited with the aim of retrieving relevant services faster. Tagging of Web Services at finer levels of granularity can assist in drawing more detailed conclusions, making their use easier. In this paper, we go beyond global Web Service tagging by proposing a classification technique for the annotation of Web Service sections. The process focuses on port type, operation and message sections of the service description. Service information as offered through WSDL and the provider and user descriptions is considered. The initial evaluation results demonstrate the efficiency of the approach in providing annotations at these different service levels.},
  doi           = {10.1109/ICWS.2014.92},
  keywords      = {Web services;information retrieval;pattern classification;WSDL;Web service section annotation;combined classification;domain-specific applications;relevant service retrieval;service information;Data mining;Documentation;Feature extraction;Noise;Semantics;Tagging;Web services;WSDL;annotation;classification;classifier ensemble;tagging;web services},
}

@InProceedings{thiagarajan_etal_2014,
  author        = {J. J. Thiagarajan and K. N. Ramamurthy and P. Sattigeri and P. T. Bremer and A. Spanias},
  title         = {Automatic image annotation using inverse maps from semantic embeddings},
  booktitle     = {2014 IEEE International Conference on Image Processing (ICIP)},
  year          = {2014},
  pages         = {3107-3111},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {Human annotation in large scale image databases is time-consuming and error-prone. Since it is very hard to mine image databases using just visual features or textual descriptors, it is common to transform the image features into a semantically meaningful space. In this paper, we propose to perform image annotation in a semantic space inferred based on sparse representations. By constructing a semantic embedding for the visual features, that is constrained to be close to the tag embedding, we show that a robust inverse map can be used to predict the tags. Experiments using standard datasets show the effectiveness of the proposed approach in automatic image annotation when compared to existing methods.},
  doi           = {10.1109/ICIP.2014.7025628},
  issn          = {1522-4880},
  keywords      = {feature extraction;image representation;visual databases;automatic image annotation;human annotation;image databases;image features;robust inverse map;semantic embeddings;semantic space;sparse representations;tag embedding;visual features;Feature extraction;Prediction algorithms;Semantics;Sparse matrices;Training;Vectors;Visualization;Image annotation;RBF interpolation;embedding;inverse map;sparse coding, rank1},
}

@InProceedings{pliakos_kotropoulos_2014a,
  author        = {K. Pliakos and C. Kotropoulos},
  title         = {Simultaneous image tagging and geo-location prediction within hypergraph ranking framework},
  booktitle     = {2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2014},
  pages         = {6894-6898},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {The development of social media has led to a burst of interest in image-related metadata information, such as tags and geo-tags. Tags are semantic keywords that are assigned to an image. Image tagging enables the users of social media sharing platforms to annotate images, facilitating image search and content description. Despite the volume of related research, issues such as accuracy or efficiency still remain open problems. Here, a novel method for simultaneous image tagging and geo-location prediction is proposed that is based on hypergraph learning. The method is further improved by enforcing group sparsity constraints. It fully exploits various types of information, such as social, image-related metadata, or similarities based on visual attributes. Experiments on a dataset crawled from Flickr demonstrate F1 at 10 top ranked tags equal to 0.558 for image tagging and cumulative geotagging prediction rate at 3 top ranks equal to 83%.},
  doi           = {10.1109/ICASSP.2014.6854936},
  issn          = {1520-6149},
  keywords      = {graph theory;image retrieval;learning (artificial intelligence);meta data;social networking (online);Flickr;content description;cumulative geotagging prediction;geo-location prediction;group sparsity constraints;hypergraph learning;hypergraph ranking framework;image annotation;image search;image-related metadata information;semantic keywords;simultaneous image tagging;social media sharing platforms;visual attributes;Correlation;Ground penetrating radar;Joints;Media;Multimedia communication;Tagging;Vectors;Geo-coordinate prediction;Group Sparsity Optimization;Hypergraph;Recommender systems;Tagging},
}

@Article{zhang_etal_2014,
  author        = {Yin Zhang and Deng Yi and Baogang Wei and Yueting Zhuang},
  title         = {A GPU-accelerated non-negative sparse latent semantic analysis algorithm for social tagging data},
  journal       = {Information Sciences},
  year          = {2014},
  volume        = {281},
  pages         = {687 - 702},
  issn          = {0020-0255},
  note          = {Multimedia Modeling},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.ins.2014.04.047},
  keywords      = {Non-negative Sparse LSA, Social tagging, GPU computing, Tag recommendation, Image classification, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S002002551400512X},
}

@Article{li_etal_2014a,
  author        = {Zechao Li and Jing Liu and Jinhui Tang and Hanqing Lu},
  title         = {Projective Matrix Factorization with unified embedding for social image tagging},
  journal       = {Computer Vision and Image Understanding},
  year          = {2014},
  volume        = {124},
  pages         = {71 - 78},
  issn          = {1077-3142},
  note          = {Large Scale Multimedia Semantic Indexing},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.cviu.2014.02.001},
  keywords      = {Projective Matrix Factorization, Unified embedding, Structure preserving, Social tagging, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S1077314214000253},
}

@Article{wang_etal_2014,
  author        = {Jingdong Wang and Jiazhen Zhou and Hao Xu and Tao Mei and Xian-Sheng Hua and Shipeng Li},
  title         = {Image tag refinement by regularized latent Dirichlet allocation},
  journal       = {Computer Vision and Image Understanding},
  year          = {2014},
  volume        = {124},
  pages         = {61 - 70},
  issn          = {1077-3142},
  note          = {Large Scale Multimedia Semantic Indexing},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.cviu.2014.02.011},
  keywords      = {Image tag refinement, Visual affinity, Regularized latent Dirichlet allocation, rank2},
  url           = {http://www.sciencedirect.com/science/article/pii/S107731421400037X},
}

@Article{belem_etal_2014,
  author        = {Fabiano M. Belém and Eder F. Martins and Jussara M. Almeida and Marcos A. Gonçalves},
  title         = {Personalized and object-centered tag recommendation methods for Web 2.0 applications},
  journal       = {Information Processing \& Management},
  year          = {2014},
  volume        = {50},
  number        = {4},
  pages         = {524 - 553},
  issn          = {0306-4573},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.ipm.2014.03.002},
  keywords      = {Tag recommendation, Relevance metrics, Personalization, rank3},
  url           = {http://www.sciencedirect.com/science/article/pii/S0306457314000181},
}

@InProceedings{zhao_etal_2014,
  author        = {Y. Zhao and Y. Zhao and R. Lin and H. Zou},
  title         = {Mining Service Tags with Enriched Information from the Internet},
  booktitle     = {2014 IEEE World Congress on Services},
  year          = {2014},
  pages         = {265-270},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Recently it receives extensive concerns that mining tags from WSDL documents, since service tags are widely used for better utilization of web services. However, most WSDL documents lack contents, which lead to great limitation for service tag mining. In this paper, we propose a novel approach to retrieve and extract service tags by leveraging information from the Internet. Our approach retrieves service related web pages by searching online for the URL of the services, then executes a Vector Space Model (VSM) based procedure to fulfill structure analysis and content extraction of the pages, finally mines service related tags together with WSDL documents. Experiments on real web services demonstrate the effectiveness of our approach, which is able to extract more number of abundant and meaningful tags.},
  doi           = {10.1109/SERVICES.2014.55},
  issn          = {2378-3818},
  keywords      = {Web services;data mining;Internet;WSDL document;Web services;service tag mining;vector space model;Data mining;Feature extraction;HTML;Vectors;Web pages;Web services;Mining;Tag;Vector Space Model;Web Service;Web pages},
}

@InProceedings{lin_cheung_2014,
  author        = {M. Lin and D. W. Cheung},
  title         = {Automatic Tagging Web Services Using Machine Learning Techniques},
  booktitle     = {2014 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year          = {2014},
  volume        = {2},
  pages         = {258-265},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Web services have become popular and increasingly important in e-business and e-commerce applications especially in large scale distributed systems. As a result, increasing number of web services has been developed. However, this abundance creates a vast collection of web services which makes the task of locating a suitable one more challenging and more difficult. Automatic clustering of web services groups together web services with similar functions. Clustering could greatly boost the power of web service search engines and generate tags to improve the search accuracy of tag-based service recommendation. In this paper, we propose a web service clustering technique based on Carrot search clustering and K-means to group similar services together to generate tags and we use naive bayes algorithm to classify web services. We also develop a tag-based service recommendation for WSDL documents. We demonstrate that the proposed clustering approach is effective for web service discovery.},
  doi           = {10.1109/WI-IAT.2014.106},
  keywords      = {Bayes methods;Web services;document handling;learning (artificial intelligence);pattern classification;pattern clustering;recommender systems;Carrot search clustering;K-means clustering;WSDL documents;Web service classification;Web service clustering technique;Web service discovery;automatic Web service tagging;machine learning techniques;naive Bayes algorithm;similar services group;tag-based service recommendation;Clustering algorithms;Feature extraction;Search engines;Tagging;Vectors;Web services;XML;Clustering;Web Service},
}

@InProceedings{wang_etal_2014a,
  author        = {S. Wang and D. Lo and B. Vasilescu and A. Serebrenik},
  title         = {EnTagRec: An Enhanced Tag Recommendation System for Software Information Sites},
  booktitle     = {2014 IEEE International Conference on Software Maintenance and Evolution},
  year          = {2014},
  pages         = {291-300},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Software engineers share experiences with modern technologies by means of software information sites, such as Stack Overflow. These sites allow developers to label posted content, referred to as software objects, with short descriptions, known as tags. However, tags assigned to objects tend to be noisy and some objects are not well tagged. To improve the quality of tags in software information sites, we propose EnTagRec, an automatic tag recommender based on historical tag assignments to software objects and we evaluate its performance on four software information sites, Stack Overflow, Ask Ubuntu, Ask Different, and Free code. We observe that that EnTagRec achieves Recall@5 scores of 0.805, 0.815, 0.88 and 0.64, and Recall@10 scores of 0.868, 0.876, 0.944 and 0.753, on Stack Overflow, Ask Ubuntu, Ask Different, and Free code, respectively. In terms of Recall@5 and Recall@10, averaging across the 4 datasets, EnTagRec improves Tag Combine, which is the state of the art approach, by 27.3% and 12.9% respectively.},
  doi           = {10.1109/ICSME.2014.51},
  issn          = {1063-6773},
  keywords      = {Web sites;operating systems (computers);public domain software;recommender systems;software maintenance;software reusability;EnTagRec;Recall@S scores;TagCombine;ask Ubuntu;ask different;automatic tag recommender;component reuse;enhanced tag recommendation system;freecode;historical tag assignments;online media;open source repositories;software creation;software information sites;software maintenance;software objects;stack overflow;Amplitude shift keying;Bayes methods;Java;Linux;Software;Training;Training data;recommendation systems;software information sites;tagging, rank1},
}

@Conference{li_etal_2014,
  author          = {Li, Y. and Xiong, J. and Liu, X. and Zhang, H. and Zhang, P.},
  title           = {Folksonomy-based in-depth annotation of web services},
  booktitle       = {2014 IEEE 8th International Symposium on Service Oriented System Engineering},
  year            = {2014},
  pages           = {243-249},
  month           = {April},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Semantic web service technologies have been proposed to enable automatic web service discovery and composition. But such approaches are suffered from significant effort to construct domain ontologies and to annotate web services with semantics by third parties. Hence social and collaborative tagging systems have been gaining the popularity on the web. Folksonomy-based web service annotating is emerging, i.e. to annotate web services with semantic from community-generated folksonomies. This paper focuses on how to provide folksonomy-based in-depth annotation of web services. Herein, the in-depth means the annotation is based on a structured folksonomy, and steps inside different parts of the web services in an automatic way. Two problems need addressed: exploring semantics for the folksonomy from original tags of web services, and automatically assigning tags to the different parts of web services. The paper proposes an approach to achieving automatic tags assignment of web services with a structured folksonomy. Such in-depth annotation facilitates web services discovery and composition by providing precise tagging of input, output and etc. A case-study and result of experiments on the pairs of tag-service extracted from a web service portal, seekda, illustrates the effectiveness of the approach. © 2014 IEEE.},
  affiliation     = {University of Chinese Academy of Sciences, Beijing 100080, China; Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China; National Computer Network Emergency Response Technical Team/Coordination Center, Beijing 100029, China; Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100093, China},
  art_number      = {6830910},
  author_keywords = {collaborative tagging; folksonomy; semantic annotation; web service},
  document_type   = {Conference Paper},
  doi             = {10.1109/SOSE.2014.32},
  journal         = {Proceedings - IEEE 8th International Symposium on Service Oriented System Engineering, SOSE 2014},
  keywords        = {Web services;information analysis;semantic Web;Web service portal;automatic Web service composition;automatic Web service discovery;automatic tags assignment;collaborative tagging systems;community-generated folksonomies;folksonomy-based Web service annotation;folksonomy-based in-depth annotation;seekda;semantic Web service technologies;tag-service;third parties;Cities and towns;Collaboration;Meteorology;Ontologies;Semantics;Tagging;Web services;collaborative tagging;folksonomy;semantic annotation;web service},
  source          = {Scopus},
  url             = {http://ieeexplore.ieee.org/document/6830912/},
}

@Article{font_etal_2014,
  author          = {Font, F. and Serrà, J. and Serra, X.},
  title           = {Class-based tag recommendation and user-based evaluation in online audio clip sharing},
  journal         = {Knowledge-Based Systems},
  year            = {2014},
  volume          = {67},
  pages           = {131-142},
  issn            = {0950-7051},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Online sharing platforms often rely on collaborative tagging systems for annotating content. In this way, users themselves annotate and describe the shared contents using textual labels, commonly called tags. These annotations typically suffer from a number of issues such as tag scarcity or ambiguous labelling. Hence, to minimise some of these issues, tag recommendation systems can be employed to suggest potentially relevant tags during the annotation process. In this work, we present a tag recommendation system and evaluate it in the context of an online platform for audio clip sharing. By exploiting domain-specific knowledge, the system we present is able to classify an audio clip among a number of predefined audio classes and to produce specific tag recommendations for the different classes. We perform an in-depth user-based evaluation of the recommendation method along with two baselines and a former version that we described in previous work. This user-based evaluation is further complemented with a prediction-based evaluation following standard information retrieval methodologies. Results show that the proposed tag recommendation method brings a statistically significant improvement over the previous method and the baselines. In addition, we report a number of findings based on the detailed analysis of user feedback provided during the evaluation process. The considered methods, when applied to real-world collaborative tagging systems, should serve the purpose of consolidating the tagging vocabulary and improving the quality of content annotations. © 2014 Elsevier B.V. All rights reserved.},
  affiliation     = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Artificial Intelligence Research Institute (IIIA-CSIC), Spanish National Research Council, Bellaterra, Spain},
  author_keywords = {Collaborative tagging; Folksonomy; Freesound; Tag recommendation; User study},
  document_type   = {Article},
  doi             = {10.1016/j.knosys.2014.06.003},
  keywords        = {Collaborative tagging, Tag recommendation, User study, Folksonomy, Freesound},
  source          = {Scopus},
  url             = {http://www.sciencedirect.com/science/article/pii/S0950705114002214?via%3Dihub},
}

@InProceedings{zhang_yu_2014,
  author          = {W. Zhang and L. Yu},
  title           = {Hybrid Personalized Tag Recommendation Algorithm Design and Evaluation},
  booktitle       = {2014 International Conference on Management of e-Commerce and e-Government},
  year            = {2014},
  pages           = {61-64},
  month           = {Oct},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {With the widespread popularity of Web 2.0 website, Collaborative Tagging System, as one of the famous social software, is getting more attention now. Tag recommendation technology has been developed to help users with tagging process recently. However, most of the tag recommendation methods are merely based on the content of tagged resource. Aiming at this limitation, a hybrid personalized tag recommendation algorithm based on user preference and content is proposed. The experiments show that this algorithm has good robustness and high degree of automation. And solve the tag system of a righteous.},
  affiliation     = {School of Information, Central University of Finance and Economics, Beijing, China; School of Information, Renmin University of China, Beijing, China},
  art_number      = {7046891},
  author_keywords = {Collaborative tagging systems; tag recommendation; Web 2.0},
  document_type   = {Conference Paper},
  doi             = {10.1109/ICMeCG.2014.22},
  journal         = {Proceedings - 2014 International Conference on Management of e-Commerce and e-Government, ICMeCG 2014},
  keywords        = {Internet;identification technology;recommender systems;social networking (online);Web 2.0;Web site;collaborative tagging system;degree-of-automation;hybrid personalized tag recommendation algorithm;social software;tag recommendation technology;Algorithm design and analysis;Collaboration;Educational institutions;Tagging;Testing;Training data;Uniform resource locators;Web 2.0;collaborative tagging systems;tag recommendation},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84949927577&doi=10.1109%2fICMeCG.2014.22&partnerID=40&md5=03203dbdab00363ff4d1238e26f54a18},
}

@Article{kim_kim_2014,
  author        = {Hyunwoo Kim and Hyoung-Joo Kim},
  title         = {A framework for tag-aware recommender systems},
  journal       = {Expert Systems with Applications},
  year          = {2014},
  volume        = {41},
  number        = {8},
  pages         = {4000 - 4009},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  abstract      = {In social tagging system, a user annotates a tag to an item. The tagging information is utilized in recommendation process. In this paper, we propose a hybrid item recommendation method to mitigate limitations of existing approaches and propose a recommendation framework for social tagging systems. The proposed framework consists of tag and item recommendations. Tag recommendation helps users annotate tags and enriches the dataset of a social tagging system. Item recommendation utilizes tags to recommend relevant items to users. We investigate association rule, bigram, tag expansion, and implicit trust relationship for providing tag and item recommendations on the framework. The experimental results show that the proposed hybrid item recommendation method generates more appropriate items than existing research studies on a real-world social tagging dataset.},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2013.12.019},
  keywords      = {Recommendation, Social tagging system, Tags, Hybrid framework, rank2},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417413009949},
}

@InProceedings{al-dhelaan_alhawasi_2015,
  author        = {M. Al-Dhelaan and H. Alhawasi},
  title         = {Graph Summarization for Hashtag Recommendation},
  booktitle     = {2015 3rd International Conference on Future Internet of Things and Cloud},
  year          = {2015},
  pages         = {698-702},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Hash tag recommendation is the problem of finding interesting hash tags for a user, which are not easily found via Twitter search. Searching a hash tag simply shows a list of tweets, each contains the query hash tag string. To find even more relevant hash tags, we propose to use a graph-based approach to find similar hash tags by using the social network graph around hash tags. We start by using a heterogeneous social graph that contains users, tweets, and hash tags, then we summarize the graph to a hash tag graph that shows the similarity between different hash tags. Finally, we rank the vertices in respect to a query hash tag using a random walk with restart and a content similarity measure. The experimental work demonstrates the effectiveness of our approach compared to baselines.},
  doi           = {10.1109/FiCloud.2015.61},
  keywords      = {graph theory;query processing;recommender systems;social networking (online);Twitter search;content similarity measure;graph summarization;graph-based approach;hash tag graph;hash tag string;hashtag recommendation;query hash tag;random walk;social graph;social network graph;Damping;Data mining;Tagging;Twitter;Web sites;Hashtag Graph;Hashtag Ranking;Hashtag Recommendation;Hashtag Search},
}

@InProceedings{pham_etal_2015,
  author        = {T. A. N. Pham and X. Li and G. Cong and Z. Zhang},
  title         = {A general graph-based model for recommendation in event-based social networks},
  booktitle     = {2015 IEEE 31st International Conference on Data Engineering},
  year          = {2015},
  pages         = {567-578},
  month         = {April},
  __markedentry = {[felipe:1]},
  abstract      = {Event-based social networks (EBSNs), such as Meetup and Plancast, which offer platforms for users to plan, arrange, and publish events, have gained increasing popularity and rapid growth. EBSNs capture not only the online social relationship, but also the offline interactions from offline events. They contain rich heterogeneous information, including multiple types of entities, such as users, events, groups and tags, and their interaction relations. Three recommendation tasks, namely recommending groups to users, recommending tags to groups, and recommending events to users, have been explored in three separate studies. However, none of the proposed methods can handle all the three recommendation tasks. In this paper, we propose a general graph-based model, called HeteRS, to solve the three recommendation problems on EBSNs in one framework. Our method models the rich information with a heterogeneous graph and considers the recommendation problem as a query-dependent node proximity problem. To address the challenging issue of weighting the influences between different types of entities, we propose a learning scheme to set the influence weights between different types of entities. Experimental results on two real-world datasets demonstrate that our proposed method significantly outperforms the state-of-the-art methods for all the three recommendation tasks, and the learned influence weights help understanding user behaviors.},
  doi           = {10.1109/ICDE.2015.7113315},
  issn          = {1063-6382},
  keywords      = {graph theory;learning (artificial intelligence);recommender systems;social networking (online);EBSN;HeteRS;event-based social networks;events recommendation;general graph-based model;groups recommendation;heterogeneous graph;learning scheme;query-dependent node proximity problem;tags recommendation;Irrigation},
}

@InProceedings{hong_etal_2015,
  author        = {H. K. Hong and K. W. Park and D. H. Lee},
  title         = {A Novel Semantic Tagging Technique Exploiting Wikipedia-Based Associated Words},
  booktitle     = {2015 IEEE 39th Annual Computer Software and Applications Conference},
  year          = {2015},
  volume        = {3},
  pages         = {648-649},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {With the rise in popularity of smart devices such as smartphones and tablet PCs, the volume of multimedia contents is growing exponentially through social networks and multimedia contents sharing systems. However, the characteristics of mobile devices and tags lead to the performance degradation of the tag-based image retrieval. In this paper, we propose a novel semantic tagging method exploiting Wikipedia-based associated words. The proposed system consists of two phases. In association words construction phase, extraction manager parses the page information from Wikipedia dump, and then semantic analyzer constructs the associated words using disambiguation page. In tag recommendation phase, a user selects single meaning among various associated words, so that associated words which belong to the chosen cluster are automatically assigned to the contents. The proposed system makes it possible to annotate the multimedia contents with the reliable and relevant tags in the smart device environment.},
  doi           = {10.1109/COMPSAC.2015.87},
  keywords      = {multimedia computing;smart phones;social networking (online);Wikipedia dump;Wikipedia-based associated word;disambiguation page;mobile device;multimedia content sharing system;performance degradation;semantic analyzer;semantic tagging method;semantic tagging technique;smart device environment;smartphone;social network;tablet PC;tag recommendation phase;tag-based image retrieval;Electronic publishing;Encyclopedias;Internet;Multimedia communication;Semantics;Tagging;Associated Words;Multimedia Tagging;Semantic Tagging;Wikipedia},
}

@InProceedings{liu_forss_2015,
  author        = {S. Liu and T. Forss},
  title         = {Automatic tag extraction from social media for visual labeling},
  booktitle     = {2015 7th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K)},
  year          = {2015},
  volume        = {01},
  pages         = {504-510},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Visual labeling or automated visual annotation is of great importance to the efficient access and management of multimedia content. Many methods and techniques have been proposed for image annotation in the last decade and they have shown reasonable performance on standard datasets. Great progress has been made especially in recent couple of years with the development of deep learning models for image content analysis and extraction of content-based concept labels. However, concept objects labels are much more friendly to machine than to users. We consider that more relevant and user-friendly visual labels need to include “context” descriptors. In this study we explore the possibilities to leverage social media content as a resource for visual labeling. We developed a tag extraction system that applies heuristic rules and term weighting method to extract image tags from associated Tweet. The system retrieves tweet-image pairs from public Twitter accounts, analyzes the Tweet, and generates labels for the images. We elaborate on different visual labeling methods, tag analysis and tag refinement methods.},
  keywords      = {Context;Labeling;Media;Organizations;Tagging;Twitter;Visualization;Image Annotation;Social Media Analysis;Twitter;Visual Labeling},
  url           = {http://ieeexplore.ieee.org/document/7526962/},
}

@InProceedings{panagopoulos_kotropoulos_2015,
  author        = {M. Panagopoulos and C. Kotropoulos},
  title         = {Image tagging using tensor decomposition},
  booktitle     = {2015 6th International Conference on Information, Intelligence, Systems and Applications (IISA)},
  year          = {2015},
  pages         = {1-5},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Social media growing has resulted into a huge amount of information. Meta-data, accompanying the raw data, can assist data manipulation and processing, e.g. the tags assigned to social images. Many systems for automatic image tagging are based on pair-wise relations. Recent approaches focus on relations among multiple vertices (i.e. items), which are respresented as hyperedges in a hypergraph. In this work, an image tagging methodology is proposed that exploits a factorization of a tensor, capturing high-order relations among multiple Flickr images. The proposed approach uses an extended graph, where the vertices represent images as well as users. By analysing the data communities and their similarities, image annotation is proposed.},
  doi           = {10.1109/IISA.2015.7388124},
  keywords      = {image processing;matrix decomposition;multimedia computing;social networking (online);Flickr images;automatic image tagging;data manipulation;data processing;factorization;hyperedges;hypergraph;meta-data;raw data;social media growing;tensor decomposition;Electronic mail;Matrix decomposition;Media;Sparse matrices;Tagging;Tensile stress},
}

@InProceedings{samarawickrama_etal_2015,
  author        = {S. Samarawickrama and S. Karunasekera and A. Harwood},
  title         = {Finding High-Level Topics and Tweet Labeling Using Topic Models},
  booktitle     = {2015 IEEE 21st International Conference on Parallel and Distributed Systems (ICPADS)},
  year          = {2015},
  pages         = {242-249},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Making sense of Twitter data streams is challenging due to the extremely high volume of data. One way to address this challenge is to consider these data streams as containing a set of high-level topics. In this research we address the problem of: given a collection of tweets about K high-level topics, how to find topic words that describe these topics as well as how to label each tweet with one of the K topics using a topic modeling approach. Current research has shown that applying topic modeling algorithms directly on tweets does not lead to good results. Hence one approach is to group related tweets together, so as to form a single “pseudo-document”, which is more informative than a single tweet. In this paper we evaluate different grouping schemes found in the literature and propose a new grouping scheme utilizing named entities and word collocations. Results show that our proposed scheme performs better than the existing approaches, to a some extent for all the test cases, and for both finding high-level topics and tweet labeling.},
  doi           = {10.1109/ICPADS.2015.38},
  keywords      = {social networking (online);text analysis;word processing;Twitter data streams;grouping scheme;grouping schemes;high-level topic model;named entities;pseudodocument;topic modeling algorithms;tweet labeling;word collocations;Blogs;Distributed databases;Labeling;Media;Resource management;Tagging;Twitter;Twitter;content-analysis;microblogging;social media mining;topic-modeling},
}

@Article{feng_etal_2015,
  author        = {S. Feng and Z. Feng and R. Jin},
  title         = {Learning to Rank Image Tags With Limited Training Examples},
  journal       = {IEEE Transactions on Image Processing},
  year          = {2015},
  volume        = {24},
  number        = {4},
  pages         = {1223-1234},
  month         = {April},
  issn          = {1057-7149},
  __markedentry = {[felipe:1]},
  abstract      = {With an increasing number of images that are available in social media, image annotation has emerged as an important research topic due to its application in image matching and retrieval. Most studies cast image annotation into a multilabel classification problem. The main shortcoming of this approach is that it requires a large number of training images with clean and complete annotations in order to learn a reliable model for tag prediction. We address this limitation by developing a novel approach that combines the strength of tag ranking with the power of matrix recovery. Instead of having to make a binary decision for each tag, our approach ranks tags in the descending order of their relevance to the given image, significantly simplifying the problem. In addition, the proposed method aggregates the prediction models for different tags into a matrix, and casts tag ranking into a matrix recovery problem. It introduces the matrix trace norm to explicitly control the model complexity, so that a reliable prediction model can be learned for tag ranking even when the tag space is large and the number of training images is limited. Experiments on multiple well-known image data sets demonstrate the effectiveness of the proposed framework for tag ranking compared with the state-of-the-art approaches for image annotation and tag ranking.},
  doi           = {10.1109/TIP.2015.2395816},
  keywords      = {image classification;learning (artificial intelligence);matrix algebra;image annotation;image tag ranking;learning;matrix recovery problem;Acceleration;Complexity theory;Optimization;Predictive models;Semantics;Training;Visualization;Automatic image annotation;automatic image annotation;low-rank;matrix recovery;tag ranking;trace norm},
}

@InProceedings{charte_etal_2015,
  author        = {F. Charte and A. J. Rivera and M. J. del Jesus and F. Herrera},
  title         = {QUINTA: A question tagging assistant to improve the answering ratio in electronic forums},
  booktitle     = {IEEE EUROCON 2015 - International Conference on Computer as a Tool (EUROCON)},
  year          = {2015},
  pages         = {1-6},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {The Web is broadly used nowadays to obtain information about almost any topic, from scientific procedures to cooking recipes. Electronic forums are very popular, with thousands of questions asked and answered every day. Correctly tagging the questions posted by users usually produces quicker and better answers by other users and experts. In this paper a prototype of a system aimed to assist the users while tagging their questions is proposed. To accomplish this task, firstly the text of each post is processed to produce a multilabel dataset, then a lazy nearest neighbor multilabel classification algorithm is used to predict the tags on new posts. The obtained results are promising, opening the door to the developing of a full automated system for this task.},
  doi           = {10.1109/EUROCON.2015.7313677},
  keywords      = {Web sites;data mining;pattern classification;question answering (information retrieval);text analysis;QUINTA;answering ratio improvement;electronic forums;lazy nearest neighbor multilabel classification algorithm;multilabel dataset;post-text processing;question tagging assistant;tag prediction;Computer science;Electronic mail;Measurement;Pipelines;Prediction algorithms;Tagging;Text mining, rank1},
}

@Article{rad_jamzad_2015,
  author        = {R. Rad and M. Jamzad},
  title         = {Automatic image annotation by a loosely joint non-negative matrix factorisation},
  journal       = {IET Computer Vision},
  year          = {2015},
  volume        = {9},
  number        = {6},
  pages         = {806-813},
  issn          = {1751-9632},
  __markedentry = {[felipe:1]},
  abstract      = {Nowadays, the number of digital images has increased so that the management of this volume of data needs an efficient system for browsing, categorising and searching. Automatic image annotation is designed for assigning tags to images for more accurate retrieval. Non-negative matrix factorisation (NMF) is a traditional machine learning technique for decomposing a matrix into a set of basis and coefficients under the non-negative constraints. In this study, the authors propose a two-step algorithm for designing an automatic image annotation system that employs the NMF framework for its first step and a variant of K-nearest neighbourhood as its second step. In the first step, a new multimodal NMF algorithm is proposed to extract the latent factors which reflect the content of images. This is done by jointly factorising the visual and textual data feature matrices so that they have close representation, although not necessarily the same. In the second step, after mapping images to the latent factors space a few tags are predicted for the new images based on a weighted average of similar data. They evaluated the performance of the proposed method and compared it to the state-of-the-art literature. Comparison results demonstrate the effectiveness and potential of the proposed method in image annotation applications.},
  doi           = {10.1049/iet-cvi.2014.0413},
  keywords      = {image classification;image retrieval;learning (artificial intelligence);matrix decomposition;visual databases;K-nearest neighbourhood;automatic image annotation system;digital images;machine learning technique;multimodal NMF algorithm;nonnegative matrix factorisation;textual data feature matrices;visual data feature matrices},
  url           = {http://ieeexplore.ieee.org/abstract/document/7328507/},
}

@InProceedings{gonzalez_etal_2015,
  author        = {J. R. C. González and J. J. F. Romero and M. G. Guerrero and F. Calderón},
  title         = {Multi-class multi-tag classifier system for StackOverflow questions},
  booktitle     = {2015 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)},
  year          = {2015},
  pages         = {1-6},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {This work approaches the text document classification problem derived from the contest “Identify Keywords and Tags from Millions of Text Questions”, published on the website Kaggle. Using data from the StackOverflow website, the problem is to predict the tags assigned to questions. This categorization is multi-class and multi-tag, which means, a question can be assigned to different topics and can also have several tags. To solve this problem, we propose a 5-way multi-class classifier system. The results obtained by this classification scheme are discussed, by analysing certain score metrics of the classifier system. Competitive results were obtained by the 5-way classifier system, obtaining F1 scores ranging from 0.59 to 0.76. The main contribution of this paper lies on the preprocessing (which implements the feature extraction phase) and the multi-tag multi-class classification scheme.},
  doi           = {10.1109/ROPEC.2015.7395121},
  keywords      = {feature extraction;pattern classification;text analysis;5-way multiclass classifier system;F1 scores;Identify Keywords and Tags from Millions of Text Questions contest;Kaggle;StackOverflow Web site;StackOverflow questions;categorization;feature extraction phase;multiclass multitag classifier system;multitag multiclass classification scheme;score metrics;tags prediction;text document classification problem;Classification algorithms;Feature extraction;Prediction algorithms;Support vector machines;Training;Vocabulary;Web sites, rank2},
}

@InProceedings{pantraki_kotropoulos_2015,
  author        = {E. Pantraki and C. Kotropoulos},
  title         = {Automatic image tagging and recommendation via PARAFAC2},
  booktitle     = {2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP)},
  year          = {2015},
  pages         = {1-6},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {An important aspect when sharing images in social networks is the tags the images are annotated with. Another closely related problem is the ability to successfully recommend images to users. An automatic image annotation and recommendation system is proposed based on Parallel Factor Analysis 2 (PARAFAC2). Here, PARAFAC2 is applied to a collection of three matrices, namely the image-feature matrix, whose columns are representations capturing the visual appearance of images, the image-tag matrix, whose columns indicate the tags associated with each image, and the image-user matrix, whose columns identify who has uploaded or is associated to each image. PARAFAC2 is able to harness the multi-tag and the multi-user information for reducing the dimensionality of the feature vectors extracted from the images. That is, by projecting the feature vector onto the semantic space derived via PARAFAC2, a sketch (i.e., a coefficient vector of reduced dimensions) is obtained. To predict the tags to be assigned to a test image, the test image sketch is multiplied by the left singular vectors of the image-tag matrix, yielding a tag vector. Similarly, to recommend users who might be interested to a test image, the sketch is multiplied by the left singular vectors of the image-user matrix, yielding a recommendation vector. Promising results are demonstrated when the aforementioned framework is applied to an image dataset of Greek popular tourist landmarks extracted from Flickr, using a 10 fold cross-validation experimental protocol.},
  doi           = {10.1109/MLSP.2015.7324363},
  issn          = {1551-2541},
  keywords      = {feature extraction;image processing;matrix algebra;recommender systems;social networking (online);Flickr;Greek popular tourist landmark;PARAFAC2;automatic image annotation;automatic image tagging;cross-validation experimental protocol;feature vector extraction;image recommendation;image-feature matrix;image-tag matrix;image-user matrix;multitag information;multiuser information;parallel factor analysis 2;recommendation system;recommendation vector;singular vector;social network;tag vector;test image sketch;visual appearance;Feature extraction;Measurement;Semantics;Sparse matrices;Tagging;Tensile stress;Training;Automatic Image Tagging;Image Recommendation;Multi-label Classification;PARAFAC2},
}

@InProceedings{kataria_agarwal_2015,
  author        = {S. Kataria and A. Agarwal},
  title         = {Distributed Representations for Content-Based and Personalized Tag Recommendation},
  booktitle     = {2015 IEEE International Conference on Data Mining Workshop (ICDMW)},
  year          = {2015},
  pages         = {1388-1395},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {We consider the problem of learning distributed representations for documents from their content and associated tags, and of distributed representations of users from documents and tags provided by users. The documents, words, and tags are represented as low-dimensional vectors and are jointly learned with a multi-layered neural language model. We propose a two stage method where in the first stage which consists of two layers, we exploit the corpus wide topic-level information contained in tags to model one layer of neural language model and use document level words sequence information to model other layer of the proposed architecture. In the second stage, we use thus obtained document and tags representations to learn user representations. We utilize these jointly trained vector representations for personalized tag recommendation tasks. Our experiments on two widely used bookmarking datasets show a significant improvements for quality of recommendations. These continuous vector representations has the added advantages of conceptually meaningful which we show by our qualitative analysis on tag suggestion tasks.},
  doi           = {10.1109/ICDMW.2015.240},
  keywords      = {indexing;learning (artificial intelligence);natural language processing;neural nets;recommender systems;search problems;book-marking datasets;content-based recommendation;corpus wide topic-level information;distributed representation learning problem;document level word sequence information;jointly trained vector representations;low-dimensional vectors;multilayered neural language model;personalized tag recommendation tasks;two stage method;Conferences;Context;Context modeling;Optimization;Semantics;Tagging;Training, rank4},
}

@Article{horsburgh_etal_2015,
  author        = {Ben Horsburgh and Susan Craw and Stewart Massie},
  title         = {Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems},
  journal       = {Artificial Intelligence},
  year          = {2015},
  volume        = {219},
  pages         = {25 - 39},
  issn          = {0004-3702},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.artint.2014.11.004},
  keywords      = {Music recommendation, Hybrid representations},
  url           = {http://www.sciencedirect.com/science/article/pii/S0004370214001362},
}

@Article{chen_etal_2015,
  author        = {Xinxiong Chen and Zhiyuan Liu and Maosong Sun},
  title         = {Estimating translation probabilities for social tag suggestion},
  journal       = {Expert Systems with Applications},
  year          = {2015},
  volume        = {42},
  number        = {4},
  pages         = {1950 - 1959},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2014.10.002},
  keywords      = {Natural language processing, Tag suggestion, Translation model, Word alignment model, Pointwise mutual information},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417414006253},
}

@Article{zhang_etal_2015,
  author        = {Jing Zhang and Xin Liu and Li Zhuo and Chao Wang},
  title         = {Social images tag ranking based on visual words in compressed domain},
  journal       = {Neurocomputing},
  year          = {2015},
  volume        = {153},
  pages         = {278 - 285},
  issn          = {0925-2312},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2014.11.027},
  keywords      = {Social images, Tag ranking, Visual words, Compressed domain, Neighbor voting},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231214015604},
}

@Article{qian_etal_2015,
  author        = {Zhiming Qian and Ping Zhong and Runsheng Wang},
  title         = {Personalized image annotation via class-specific cross-domain learning},
  journal       = {Signal Processing: Image Communication},
  year          = {2015},
  volume        = {34},
  pages         = {61 - 71},
  issn          = {0923-5965},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.image.2015.03.008},
  keywords      = {Personalized image annotation, Cross-domain learning, Nearest neighbor model, Multiple kernel learning},
  url           = {http://www.sciencedirect.com/science/article/pii/S0923596515000442},
}

@InProceedings{wang_etal_2015a,
  author        = {X. Wang and Y. Jia and R. Chen and B. Zhou},
  title         = {Ranking User Tags in Micro-Blogging Website},
  booktitle     = {2015 2nd International Conference on Information Science and Control Engineering},
  year          = {2015},
  pages         = {400-403},
  month         = {April},
  __markedentry = {[felipe:1]},
  abstract      = {Users can annotate themselves using free tags in micro-blogging website such as Sina Weibo. The tags of a user demonstrate the characteristics of the user and are generally in a random order without any importance or relevance information. It limits the effectiveness of user tags in system recommendation and other applications. In this paper, we proposed a user tag ranking schema which is based on interactive relations between users. Influence strength between users is considered in our user tag ranking method. Relevance scores between tags and users are also utilized to rank user tags. Experiments are conducted on distributed processing framework Hadoop to process the very large Sina Weibo dataset which contains more than 140 million users. Experimental results show that our method outputs frequently used method and gives good performance.},
  doi           = {10.1109/ICISCE.2015.94},
  keywords      = {Web sites;data handling;parallel processing;recommender systems;Hadoop;Sina Weibo;distributed processing framework;microblogging Website;system recommendation;user tag ranking method;Collaboration;Distributed databases;Mathematical model;Media;Servers;Social network services;Tagging;Micro-blogging;Sina Weibo;Social Network;Tag Ranking},
}

@InProceedings{yin_etal_2015,
  author        = {Ying Yin and Yuhai Zhao and Bin Zhang},
  title         = {GDC: An efficient tag recommendation algorithm},
  booktitle     = {2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)},
  year          = {2015},
  pages         = {1382-1387},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {Diversifying the query results is an effective approach to obtain an user's underlying query information. Tag as an important recommendation information has drawn more and more attentions, which is also extensively exploited in SN(Social Network). The results generated by the current algorithms often ignore diversity in the recommended results, which seriously affecting the user's experience. In this paper, we address the problem of diversified coverage based tag recommendation. To our best knowledge, it is first proposed in the context of tag recommendation. Since the NP-hardness of the problem, a greedy based diversified coverage tag recommendation algorithm, namely GDC, is proposed. First, it constructs a semantic similarity graph based on the local and global tag co-occurrence matrices, which improves the recommendation accuracy by incorporating both the user's interests and the popularity of tags. Further, GDC recursively searches the MIDS (Minimum Independent Dominating Set) in an efficient manner together with an effective pruning rule. The experiments conducted on the real datasets of MovieLens and Last.fm show that the proposed GDC improve the diversity significantly.},
  doi           = {10.1109/FSKD.2015.7382146},
  keywords      = {computational complexity;matrix algebra;query processing;recommender systems;social networking (online);GDC;Last.fm;MIDS;MovieLens;NP-hardness;SN;diversified coverage based tag recommendation;minimum independent dominating set;query information;query results;social network;tag cooccurrence matrices;tag recommendation algorithm;Social Network;Tag recommendation;diversified coverage;greedy},
}

@Article{qian_etal_2015a,
  author        = {Z. Qian and P. Zhong and R. Wang},
  title         = {Tag Refinement for User-Contributed Images via Graph Learning and Nonnegative Tensor Factorization},
  journal       = {IEEE Signal Processing Letters},
  year          = {2015},
  volume        = {22},
  number        = {9},
  pages         = {1302-1305},
  month         = {Sept},
  issn          = {1070-9908},
  __markedentry = {[felipe:1]},
  abstract      = {Social image tagging systems mostly suffer from poor performance for image retrieval due to the noisy and incomplete correspondences between user-contributed images and their associated tags. In this letter, we aim to refine tag allocations in the social tagging data provided by these systems. In particular, we propose to harness the tagged and untagged data with a two-stage strategy according to different types of data relations, i.e. item similarity defined by prior knowledge and item co-occurrence learned from data statistics. To solve the sparsity problem, we first introduce a new graph learning (GL) method for enriching the tagging data according to item similarities. Then, we develop a method of nonnegative tensor factorization (NTF) for learning more coherent ternary relations among users, images and tags coupled by the manifold constraints learned from item co-occurrences. Experimental results with the tagging data from the NUS-WIDE dataset have been reported to validate the effectiveness of the proposed method.},
  doi           = {10.1109/LSP.2015.2399915},
  keywords      = {graph theory;image retrieval;learning (artificial intelligence);matrix decomposition;tensors;GL method;NTF method;NUS-WIDE dataset;data statistics;graph learning method;image retrieval;item co-occurrence;item similarity;manifold constraints;nonnegative tensor factorization;social image tagging systems;social tagging data;sparsity problem;tag allocations;tag refinement;two-stage strategy;user-contributed images;Image reconstruction;Manganese;Noise measurement;Resource management;Tagging;Tensile stress;Visualization;Graph learning;image retrieval;nonnegative tensor factorization;tag refinement},
}

@InProceedings{vargas-baldrich_etal_2015,
  author        = {S. Vargas-Baldrich and M. Linares-Vásquez and D. Poshyvanyk},
  title         = {Automated Tagging of Software Projects Using Bytecode and Dependencies (N)},
  booktitle     = {2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE)},
  year          = {2015},
  pages         = {289-294},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Several open and closed source repositories group software systems and libraries to allow members of particular organizations or the open source community to take advantage of them. However, to make this possible, it is necessary to have effective ways of searching and browsing the repositories. Software tagging is the process of assigning terms (i.e., tags or labels) to software assets in order to describe features and internal details, making the task of understanding software easier and potentially browsing and searching through a repository more effective. We present Sally, an automatic software tagging approach that is able to produce meaningful tags for Maven-based software projects by analyzing their bytecode and dependency relations without any special requirements from developers. We compared tags generated by Sally to the ones in two widely used online repositories, and the tags generated by a state-of-the-art categorization approach. The results suggest that Sally is able to generate expressive tags without relying on machine learning-based models.},
  doi           = {10.1109/ASE.2015.38},
  keywords      = {Internet;information retrieval;project management;public domain software;software management;Maven-based software projects;Sally;automatic software tagging approach;bytecode;categorization approach;closed source repository group software systems;dependency relations;online repositories;open source community;open source repository group software systems;software assets;software tagging;term assignment;Data mining;Feature extraction;Software algorithms;Software systems;Support vector machines;Tagging},
}

@Article{xia_etal_2015,
  author        = {Zhaoqiang Xia and Xiaoyi Feng and Jinye Peng and Jun Wu and Jianping Fan},
  title         = {A regularized optimization framework for tag completion and image retrieval},
  journal       = {Neurocomputing},
  year          = {2015},
  volume        = {147},
  pages         = {500 - 508},
  issn          = {0925-2312},
  note          = {Advances in Self-Organizing Maps Subtitle of the special issue: Selected Papers from the Workshop on Self-Organizing Maps 2012 (WSOM 2012)},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2014.06.028},
  keywords      = {Tag completion, Social image, Non-negative matrix factorization, Visual diversity, Image retrieval},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231214007747},
}

@Conference{park_etal_2015,
  author          = {Park, J. and Park, K.W. and Yun, Y. and Kim, M. and Rho, S. and Man, K.L. and Chong, W.K.},
  title           = {Efficient multimedia contents management system with tag recommendations},
  year            = {2015},
  volume          = {2},
  pages           = {737-738},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Recently, rapid development of internet technology, the amount of data on the web is exponentially generated. Because of that, users are difficult to find desired multimedia data. For efficient search and management of multimedia data, collaborative tagging is appropriated to describe and categorize the data. Then we proposed the collaborative multimedia management systems based on tag recommendation. Our system suggests the tag candidates for describing and categorizing the multimedia contents.},
  affiliation     = {Department of Multimedia, Sungkyul University, Anyang, South Korea; Computer Science and Software Engineering, Xi'an Jiaotong-Liverpool University, China; International Business School Suzhou (IBSS), Xi'an Jiaotong-Liverpool University, China},
  author_keywords = {Folksonomy; Multimedia contents management; Recommendation system; Web 2.0},
  document_type   = {Conference Paper},
  journal         = {Lecture Notes in Engineering and Computer Science},
  source          = {Scopus},
  url             = {http://www.iaeng.org/publication/IMECS2015/IMECS2015_pp737-738.pdf},
}

@Conference{chong_etal_2015,
  author          = {Chong, D. and Wu, H. and Shi, H. and He, W.},
  title           = {Dynamic tagging for enterprise knowledge sharing and representation},
  year            = {2015},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The development of Web 2.0 technology provides an easy way for people to transfer and share knowledge. As a collaborative tagging tool, folksonomy is an efficient indexing method in Web 2.0 environments. After analyzing the pros and cons of current knowledge management systems, this research proposes a dynamic Tagging system that combines folksonomy technologies with other approaches including automatic schema enrichment and training. The proposed system improves access to a large, growing collection by supporting users collaboratively contribute to the building of tags. In addition, the proposed system provides an efficient way for firms to represent knowledge and share knowledge with customers and other firms.},
  affiliation     = {Department of Information Technology and Decision Sciences, Old Dominion University, United States; Department of Management and Information Sciences, University of Southern Indiana, United States},
  author_keywords = {Collaboration; Folksonomy; Knowledge; Tagging},
  document_type   = {Conference Paper},
  journal         = {2015 Americas Conference on Information Systems, AMCIS 2015},
  source          = {Scopus},
  url             = {https://pdfs.semanticscholar.org/8842/04f9cccdaa6a6a122d4ebc612b5a961782cf.pdf},
}

@Article{zhao_etal_2015,
  author          = {Zhao, W. and Guan, Z. and Liu, Z.},
  title           = {Ranking on heterogeneous manifolds for tag recommendation in social tagging services},
  journal         = {Neurocomputing},
  year            = {2015},
  volume          = {148},
  pages           = {521-534},
  issn            = {0925-2312},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Nowadays, most social Websites allow users to annotate resources (such as Web pages and images) with keywords, i.e. tags. Collaborative tagging data reflects the semantic perception of users, thus providing valuable information for the related recommendation problems, e.g. tag recommendation, resource recommendation. In this paper, we tackle the problem of personalized tag recommendation in social tagging services by generalizing the traditional manifold ranking idea. Specifically, we model the complex relationships in tagging data as a heterogeneous graph and propose a novel ranking algorithmic framework for heterogeneous manifolds, named GRoMO (Graph-based Ranking of Multi-type interrelated Objects). In our system both the resource to be tagged (accounting for relevance) and the user[U+05F3]s historical tags (accounting for personalization) are treated as query inputs. Then tags are ranked according to the output of GRoMO and the top tags are recommended to that user. We also explore adapting GRoMO for resource recommendation. For experiments we crawled a tagging dataset from the well-known tagging service, Del.icio.us. Experimental results indicate (1) the proposed method is effective and significantly outperforms baseline methods; (2) the iterative form solutions of GRoMO converge very fast and can be used when the dataset is large; (3) GRoMO can also be used for resource recommendation. © 2014 Elsevier B.V.},
  affiliation     = {College of Information and Technology, Northwest University of China, Xi'an, Shaanxi, China; National Laboratory of Radar Signal Processing, Xidian University, Xi'an, Shaanxi, China},
  author_keywords = {Heterogeneous graphs; Manifold ranking; Recommendation; Social tagging},
  document_type   = {Article},
  doi             = {10.1016/j.neucom.2014.07.011},
  keywords        = {Manifold ranking, Heterogeneous graphs, Social tagging, Recommendation},
  source          = {Scopus},
  url             = {http://www.sciencedirect.com/science/article/pii/S0925231214008893},
}

@Article{wang_etal_2015,
  author          = {Jun Wang and Jiaxu Peng and Ou Liu},
  title           = {A classification approach for less popular webpages based on latent semantic analysis and rough set model},
  journal         = {Expert Systems with Applications},
  year            = {2015},
  volume          = {42},
  number          = {1},
  pages           = {642 - 648},
  issn            = {0957-4174},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Nowadays, with the explosive growth of web information, the webpage classification faces great challenge. Computers have difficulty in understanding the semantic meaning of textual or non-textual webpages. Fortunately, Web 2.0 based collaborative tagging system brings new opportunities to solve this problem. It abstracts structured tags from unstructured content in webpages. However, large numbers of webpages on the Internet are less popular. Their tagging information is sparse, which makes their topic unclear and leads to ambiguous classification. Illuminated by the "ambiguous classification", we name the less popular webpage "hesitant webpage". In this paper, we propose an advanced approach for hesitant webpages classification. Firstly, hesitant webpages are divided into bridges, hubs and attached webpages according to their roles on the Internet. Secondly, attached webpages are classified by mining and extending their information in two perspectives. One is the latent semantic analysis (LSA) which is applied to fully explore the semantic meaning of sparse tags. It promotes accurate cognition of webpages semantically close to attached webpages. Another is the proposed density-relation-based rough set model which measures the affiliation degree of attached webpages in different categories. Experiment on real data shows that our approach effectively classifies the hesitant webpages base on the semantic meaning. © 2014 Elsevier Ltd. All rights reserved.},
  affiliation     = {School of Economics and Management, BeiHang University, Beijing, China; School of Accounting and Finance, Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong},
  author_keywords = {Complex network analysis; Latent semantic analysis; Rough set; Webpage classification},
  document_type   = {Article},
  doi             = {http://dx.doi.org/10.1016/j.eswa.2014.08.013},
  keywords        = {Webpage classification, Complex network analysis, Rough set, Latent semantic analysis},
  source          = {Scopus},
  url             = {http://www.sciencedirect.com/science/article/pii/S0957417414004898},
}

@InProceedings{shi_etal_2016,
  author        = {M. Shi and J. Liu and D. Zhou and M. Tang and F. Xie and T. Zhang},
  title         = {A Probabilistic Topic Model for Mashup Tag Recommendation},
  booktitle     = {2016 IEEE International Conference on Web Services (ICWS)},
  year          = {2016},
  pages         = {444-451},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {Mashups are prevalent Service-Oriented Architecture (SOA) based applications consisting of multiple Web Application Programming Interfaces (APIs) and content. Tags have been extensively used to organize and index mashup services. However, people favor manual tags creation in the past. This approach demands user intervention, which is extremely time-consuming and probes to errors. In this paper we propose a novel Mashup-API-Tag model for automatic mashup tag recommendation. The model simultaneously incorporates the composition relationships between mashups and APIs as well as the annotation relationships between APIs and tags to discover the latent topics. Then the semantic similarity between Web APIs and mashups can be acquired. Subsequently, tags of chosen APIs are recommended to a mashup where the mashup and the APIs are most similar. In addition, we develop a tag filtering algorithm to select the most relevant tags for recommendation. The experimental results on a real world dataset prove that our approach outperforms other methods, including frequency-based methods and the methods that only consider the composition relationships and the annotation relationships separately.},
  doi           = {10.1109/ICWS.2016.64},
  keywords      = {Internet;application program interfaces;indexing;information filtering;recommender systems;service-oriented architecture;Web API;Web application programming interfaces;annotation relationships;composition relationships;frequency-based methods;mashup services;mashup tag recommendation;probabilistic topic model;semantic similarity;service-oriented architecture based applications;tag filtering algorithm;user intervention;Manuals;Mashups;Probabilistic logic;Semantics;Service-oriented architecture;Tagging;Web APIs;mashups;tag recommendation;topic models},
}

@InProceedings{tao_yao_2016,
  author        = {Ruilin Tao and Tianfang Yao},
  title         = {Tag recommendation based on Paragraph Vector},
  booktitle     = {2016 2nd IEEE International Conference on Computer and Communications (ICCC)},
  year          = {2016},
  pages         = {2786-2789},
  month         = {Oct},
  __markedentry = {[felipe:1]},
  abstract      = {With the rapid development of Q&A communities, the amount of questions present on Internet as “explosive” growth. Using tags to organize these questions is a reasonable and effective way. Most of traditional tag recommendations are based on bag-of-words as features. Despite their popularity, bag-of-words features ignore semantics and lose the ordering of the words. Paragraph Vector overcomes the weakness of bag-of-words model. Empirical results show that Paragraph Vector outperforms bag-of-words for tag recommendation.},
  doi           = {10.1109/CompComm.2016.7925205},
  keywords      = {question answering (information retrieval);recommender systems;vectors;Internet;Q and A communities;bag-of-words model;paragraph vector;tag recommendation;Explosives;Films;multilabel;paragraph vector;tag recommendation, rank2},
}

@InProceedings{yadav_etal_2016,
  author        = {U. Yadav and J. Kaur and N. Duhan},
  title         = {Semantically related tag recommendation using folksonomized ontology},
  booktitle     = {2016 3rd International Conference on Computing for Sustainable Global Development (INDIACom)},
  year          = {2016},
  pages         = {3419-3423},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {Folksonomy allows user to tag resources independent of vocabulary and is being widely used in social web systems. But these vocabularies have some limitations like ambiguity, variation between tags and many more. Tag based ontologies are developed to overcome such issues. This paper focuses on eliminating limitations of folksonomies and extracting semantic relationship between tags to develop ontologies. Finally semantically related tag is recommendation to user as per the user search query based on developed ontology. Also the user is recommended with the latest trending tags used in social web.},
  keywords      = {ontologies (artificial intelligence);query processing;recommender systems;folksonomized ontology;folksonomy;resources tagging;semantic relationship;semantically related tag recommendation;social Web systems;tag based ontologies;user search query;vocabulary;Clustering algorithms;Context;Electronic mail;Flickr;Internet;Ontologies;Tagging;Folksonomy;Fuzzy Clustering;Ontology;Tag Recommendation},
  url           = {http://ieeexplore.ieee.org/document/7724900/},
}

@InProceedings{mayhew_etal_2016,
  author        = {M. B. Mayhew and B. Chen and K. S. Ni},
  title         = {Assessing semantic information in convolutional neural network representations of images via image annotation},
  booktitle     = {2016 IEEE International Conference on Image Processing (ICIP)},
  year          = {2016},
  pages         = {2266-2270},
  month         = {Sept},
  __markedentry = {[felipe:1]},
  abstract      = {Image annotation, or prediction of multiple tags for an image, is a challenging task. Most current algorithms are based on large sets of handcrafted features. Deep convolutional neural networks have recently outperformed humans in image classification, and these networks can be used to extract features highly predictive of an image's tags. In this study, we analyze semantic information in features derived from two pre-trained deep network classifiers by evaluating their performance in nearest neighbor-based approaches to tag prediction. We generally exceed performance of the manual features when using the deep features. We also find complementary information in the manual and deep features when used in combination for image annotation.},
  doi           = {10.1109/ICIP.2016.7532762},
  keywords      = {feature extraction;image classification;image representation;neural nets;convolutional neural network representations;feature extraction;handcrafted features;image annotation;image classification;image tags;multiple tag prediction;semantic information;Feature extraction;Manuals;Measurement;Neural networks;Prediction algorithms;Semantics;Training;deep learning;feature representations;image annotation},
}

@InProceedings{shah_etal_2016,
  author        = {R. R. Shah and A. Samanta and D. Gupta and Y. Yu and S. Tang and R. Zimmermann},
  title         = {PROMPT: Personalized User Tag Recommendation for Social Media Photos Leveraging Personal and Social Contexts},
  booktitle     = {2016 IEEE International Symposium on Multimedia (ISM)},
  year          = {2016},
  pages         = {486-492},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Social media platforms such as Flickr allow users to annotate photos with descriptive keywords, called, tags with the goal of making multimedia content easily understandable, searchable, and discoverable. However, manual annotation is very time-consuming and cumbersome for most users, which makes it difficult to search relevant photos. Moreover, predicted tags for a photo are not necessarily relevant to users' interests. Thus, it necessitates for an automatic tag prediction system that considers users' interests and describes objective aspects of the photo such as visual content and activities. To this end, this paper presents a tag recommendation system, called, PROMPT, that recommends personalized tags for a given photo leveraging personal and social contexts. Specifically, first, we determine a group of users who have similar tagging behavior as the user of the photo, which is very useful in recommending personalized tags. Next, we find candidate tags from visual content, textual metadata, and tags of neighboring photos, and recommends five most suitable tags. We initialize scores of the candidate tags using asymmetric tag co-occurrence probabilities and normalized scores of tags after neighbor voting, and later perform random walk to promote the tags that have many close neighbors and weaken isolated tags. Finally, we recommend top five user tags to the given photo. Experimental results on a Flickr dataset (46,700 photos in the test set and 28 million photos in the train set) with 1,540 unique user tags confirm that the proposed algorithm outperforms state-of-the-arts.},
  doi           = {10.1109/ISM.2016.0109},
  keywords      = {image retrieval;meta data;multimedia computing;probability;recommender systems;social networking (online);Flickr;Flickr dataset;PROMPT;asymmetric tag co-occurrence probabilities;automatic tag prediction system;multimedia content discoverability;multimedia content searchability;multimedia content understandability;personal contexts;personalized user tag recommendation system;social contexts;social media photos;tagging behavior;textual metadata;user interests;visual content;Context;Flickr;Metadata;Multimedia communication;Tagging;Visualization;Tag recommendation;multimodal analysis;multimodal information;personalized user tags;social media},
}

@InProceedings{li_etal_2016a,
  author        = {X. Li and P. Xu and Y. Shi and M. Larson and A. Hanjalic},
  title         = {Simple tag-based subclass representations for visually-varied image classes},
  booktitle     = {2016 14th International Workshop on Content-Based Multimedia Indexing (CBMI)},
  year          = {2016},
  pages         = {1-6},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper, we present a subclass-representation approach that predicts the probability of a social image belonging to one particular class. We explore the co-occurrence of user-contributed tags to find subclasses with a strong connection to the top level class. We then project each image onto the resulting subclass space, generating a subclass representation for the image. The advantage of our tag-based subclasses is that they have a chance of being more visually stable and easier to model than top-level classes. Our contribution is to demonstrate that a simple and inexpensive method for generating sub-class representations has the ability to improve classification results in the case of tag classes that are visually highly heterogenous. The approach is evaluated on a set of 1 million photos with 10 top-level classes, from the dataset released by the ACM Multimedia 2013 Yahoo! Large-scale Flickr-tag Image Classification Grand Challenge. Experiments show that the proposed system delivers sound performance for visually diverse classes compared with methods that directly model top classes.},
  doi           = {10.1109/CBMI.2016.7500265},
  keywords      = {image classification;image representation;Flickr-tag Image Classification Grand Challenge;Yahoo;image subclass representation;social image;tag classes;tag-based subclass representations;visually-varied image classes;Flickr;Multimedia communication;Predictive models;Support vector machines;Tagging;Training;Visualization},
}

@InProceedings{li_etal_2016,
  author        = {J. Li and S. Feng and C. Lang},
  title         = {Graph regularized low-rank feature learning for robust multi-label image annotation},
  booktitle     = {2016 IEEE 13th International Conference on Signal Processing (ICSP)},
  year          = {2016},
  pages         = {102-106},
  month         = {Nov},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic image annotation has drawn much attention in the last two decades due to its application in social images organization. Most studies treat image annotation as a typical multi-label classification problem, which requires sufficient number of training samples with complete and clean tags to train reliable prediction models. Being aware of this drawback, we develop a novel graph regularized low-rank feature mapping for image annotation under semi-supervised learning framework. Specifically, our approach refers to the idea of matrix recovery and uses the matrix trace norm to capture the correlations among different tags. It also helps to control the model complexity. In addition, by using graph Laplacian regularization as a smooth operator, the proposed approach can explicitly take into account the local geometric structure on both labeled and unlabeled images. Moreover, considering the tags of labeled images tend to be missing or noisy, we introduce a supplementary ideal label matrix to solve the problems of missing tags and noisy tags for given training samples. A variety of experiments conducted on image datasets ESPGame, IAPRTC-12, and NUS-WIDE prove the effectiveness of the proposed approach.},
  doi           = {10.1109/ICSP.2016.7877805},
  issn          = {2164-5221},
  keywords      = {correlation methods;graph theory;image annotation;image classification;learning (artificial intelligence);smoothing methods;ESPGame;IAPRTC-12;NUS-WIDE;automatic image annotation;graph Laplacian regularization;graph regularized low-rank feature learning;image datasets;local geometric structure;matrix recovery;matrix trace norm;model complexity;multilabel classification problem;prediction models;robust multilabel image annotation;semisupervised learning framework;social images organization;supplementary ideal label matrix;Laplace equations;Noise measurement;Optimization;Predictive models;Training;Visualization;automatic image annotation;graph Laplacian matrix;low-rank;semi-supervised learning;trace norm},
}

@InProceedings{nwana_chen_2016,
  author        = {A. O. Nwana and T. Chen},
  title         = {QUOTE: Querying Users as Oracles in Tag Engines a Semi-Supervised Learning Approach to Personalized Image Tagging},
  booktitle     = {2016 IEEE International Symposium on Multimedia (ISM)},
  year          = {2016},
  pages         = {30-37},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {Previous work has correctly identified that many of the tags that users provide on images are not visually relevant, and subsequently assume such user generated tags are noise or irrelevant. Another assumption about user generated tags is that the order of these tags provides no useful information for the prediction of tags on future images. We challenge the aforementioned assumptions, and provide a machine learning approach to the problem of personalized image tagging with the following contributions: 1.) We reformulate the personalized image tagging problem as a search/retrieval ranking problem, 2.) We leverage the order of tags, which does not always reflect visual relevance, provided by the user in the past as a cue to their tag preferences, similar to click data, 3.) We propose a technique to augment sparse user tag data (semi-supervision), and 4.) We demonstrate the efficacy of our method on a subset of Flickr images, showing improvement over our previous methods.},
  doi           = {10.1109/ISM.2016.0016},
  keywords      = {image classification;image retrieval;learning (artificial intelligence);Flickr;QUOTE;machine learning;personalized image tagging;querying users as oracles in tag engines;search-retrieval ranking problem;semisupervised learning;Computers;Market research;Search engines;Search problems;Tagging;Visualization;Vocabulary;L2R;behavior;learning-to-rank;personalization;rankSVM;tagging;user preference},
}

@InProceedings{niu_etal_2016,
  author        = {W. Niu and J. Caverlee and H. Lu and K. Kamath},
  title         = {Community-based geospatial tag estimation},
  booktitle     = {2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)},
  year          = {2016},
  pages         = {279-286},
  month         = {Aug},
  __markedentry = {[felipe:1]},
  abstract      = {This paper tackles the geospatial tag estimation problem, which is of critical importance for location-based search, retrieval, and mining applications. However, tag estimation is challenging due to massive sparsity, uncertainty in the tags actually used, as well as diversity across locations and times. Toward overcoming these challenges, we propose a community-based smoothing approach that seeks to uncover hidden conceptual communities which link multiple related locations by their common interests in addition to their proximity. Through extensive experiments over a sample of millions of geotagged Twitter posts, we demonstrate the effectiveness of the smoothing approach and validate the intuition that geo-locations have the tendency to share similar “ideas” in the formation of conceptual communities.},
  doi           = {10.1109/ASONAM.2016.7752246},
  keywords      = {data mining;estimation theory;geographic information systems;information retrieval;mobile computing;social networking (online);Twitter posts;community-based geospatial tag estimation;location-based mining;location-based retrieval;location-based search;Estimation;Geospatial analysis;Smoothing methods;Tagging;Time measurement;Twitter},
}

@InProceedings{beyer_pinzger_2016,
  author        = {S. Beyer and M. Pinzger},
  title         = {Grouping Android Tag Synonyms on Stack Overflow},
  booktitle     = {2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR)},
  year          = {2016},
  pages         = {430-440},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {On Stack Overflow, more than 38,000 diverse tags are used to classify posts. The Stack Overflow community provides tag synonyms to reduce the number of tags that have the same or similar meaning. In our previous research, we used those synonym pairs to derive a number of strategies to create tag synonyms automatically. In this work, we continue this line of research and present an approach to group tag synonyms to meaningful topics. We represent our synonyms as directed, weighted graphs, and investigate several graph community detection algorithms to build meaningful groups of tags, also called tag communities. We apply our approach to the tags obtained from Android-related Stack Overflow posts and evaluate the resulting tag communities quantitatively with various community metrics. In addition, we evaluate our approach qualitatively through a manual inspection and comparison of a random sample of tag communities. Our results show that we can cluster the Android tags to 2,481 meaningful tag communities. We also show how these tag communities can be used to derive trends of topics of Android-related questions on Stack Overflow.},
  doi           = {10.1109/MSR.2016.051},
  keywords      = {Android (operating system);directed graphs;pattern clustering;Android tag synonym grouping;Stack Overflow;directed-weighted graphs;graph community detection algorithm;qualitative analysis;tag community metrics;Androids;Detection algorithms;Humanoid robots;Market research;Software engineering;Tagging;Terminology;Android;Clustering;Stack Overflow;Tags, rank1},
  url           = {http://ieeexplore.ieee.org/document/7832922/},
}

@InProceedings{nwana_chen_2016a,
  author        = {A. O. Nwana and Tsuhan Chen},
  title         = {Who ordered this?: Exploiting implicit user tag order preferences for personalized image tagging},
  booktitle     = {2016 IEEE International Conference on Multimedia Expo Workshops (ICMEW)},
  year          = {2016},
  pages         = {1-6},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {What makes a person pick certain tags over others when tagging an image? Does the order that a person presents tags for a given image follow an implicit bias that is personal? Can these biases be used to improve existing automated image tagging systems? We show that tag ordering, which has been largely overlooked by the image tagging community, is an important cue in understanding user tagging behavior and can be used to improve auto-tagging systems. Inspired by the assumption that people order their tags, we propose a new way of measuring tag preferences, and also propose a new personalized tagging objective function that explicitly considers a user's preferred tag orderings. We also provide a (partially) greedy algorithm that produces good solutions to our new objective and under certain conditions produces an optimal solution. We validate our method on a subset of Flickr images that spans 5000 users, over 5200 tags, and over 90,000 images. Our experiments show that exploiting personalized tag orders improves the average performance of state-of-art approaches both on per-image and per-user bases.},
  doi           = {10.1109/ICMEW.2016.7574753},
  keywords      = {greedy algorithms;identification technology;image recognition;image retrieval;Flickr images;automated image tagging systems;autotagging systems;greedy algorithm;personalized image tagging;tagging objective function;user tag order preferences;Flickr;Linear programming;Measurement;Tagging;Training;Visualization;Vocabulary;ordered tags;personalization;personalized tagging;tag mining;tag preferences;tag retrieval;user behavior, rank1},
}

@InProceedings{cai_etal_2016,
  author        = {X. Cai and J. Zhu and B. Shen and Y. Chen},
  title         = {GRETA: Graph-Based Tag Assignment for GitHub Repositories},
  booktitle     = {2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC)},
  year          = {2016},
  volume        = {1},
  pages         = {63-72},
  month         = {June},
  __markedentry = {[felipe:1]},
  abstract      = {GitHub is a well-known software community where a large number of software repositories are hosted. Since large amounts of documents and code in GitHub repositories are in a mess, users cannot search or understand them efficiently. One solution is to employ a tag system, which annotates each repository with several tags. Thus, the GitHub repositories can be more efficiently accessed and understood. However, GitHub does not provide any automated tools of tagging repositories. In order to tackle this problem, we propose GRETA, a novel graph-based approach to assigning tags for repositories on GitHub. The core insight of GRETA is (1) to construct an Entity-Tag Graph (ETG) for GitHub using the domain knowledge from StackOverflow, and (2) to assign tags for repositories by taking a random walk algorithm. We have implemented GRETA and also developed a repository search engine for GitHub using the tag assignment results of GRETA. We have evaluated GRETA against several baseline methods to investigate its effectiveness of tagging GitHub repositories. The results show GRETA achieves up to 35% of F-Measure, outperforming the baseline methods. Besides, the GRETA-based search engine gains a higher NDCG value than the search engine provided by GitHub, indicating that it significantly enhances the search ability on GitHub with the tagged repositories.},
  doi           = {10.1109/COMPSAC.2016.124},
  keywords      = {graph theory;random processes;search engines;software engineering;ETG;GRETA-based search engine;GitHub software repositories;StackOverflow;entity-tag graph;graph-based tag assignment;random walk algorithm;repository search engine;software community;Cascading style sheets;Dictionaries;Electronic mail;Search engines;Software;Software algorithms;Tagging;GitHub;Tag assignment;cross-community, rank1},
}

@InProceedings{damasevicius_etal_2016,
  author        = {R. Damaševicius and R. Valys and M. Woźniak},
  title         = {Intelligent tagging of online texts using fuzzy logic},
  booktitle     = {2016 IEEE Symposium Series on Computational Intelligence (SSCI)},
  year          = {2016},
  pages         = {1-8},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {We propose four fuzzy-logic based models of tag recommendation, which are based on the interpretation of word frequency as a fuzzy membership function, and provide experimental results of tag recommendation for a variety of text datasets using different fuzzy logic operators. The novelty of the proposed models is the use of fuzzy logic modeling concepts to define a set of tags, the use of an existing set of tags for the selection of tags to strengthen the selection of most relevant (i.e., commonly used) tags, and the possibility to use an ontology to select semantically generalized tags. A system developed using the proposed models is adaptive (adapts the recommended tags to the existing set of tags), has a feedback (after each tagging, the set of tags and the dictionary are updated), is personalized (each user develops its own set of tags), and is semantics-aware (uses an ontology to refine tags). The models are validated using five sets of texts with different topics (technology, cooking, carrier, scientific, nature) and different length.},
  doi           = {10.1109/SSCI.2016.7849917},
  keywords      = {fuzzy logic;identification technology;knowledge based systems;recommender systems;text analysis;fuzzy logic;fuzzy membership function;intelligent tagging;online texts;tag recommendation;text datasets;Collaboration;Frequency measurement;Fuzzy logic;Fuzzy sets;Semantic Web;Semantics;Tagging;fuzzy logic;recommender systems;semantic web;text mining;text tagging, rank1},
}

@Article{kiu_2016,
  author          = {Kiu, C.C.},
  title           = {Automatic tags generation in folksonomy for learning resources reuse and sharing},
  journal         = {Pertanika Journal of Science and Technology},
  year            = {2016},
  volume          = {24},
  number          = {2},
  pages           = {493-506},
  note            = {cited By},
  __markedentry   = {[felipe:5]},
  abstract        = {With the proliferation of Web 2.0 technologies, folksonomy which is also known as social tagging or collaborative tagging is widely used by learners to annotate and categorize their learning resources. In a folksonomy system, the tags are added by learners to the learning resources, hence the tags are often ambiguous, overly personalised and imprecise. In addition, conjugated words, compound words and nonsense words may be used in tagging and shared among a group of learners. This has resulted in an uncontrolled and chaotic set of tagging terms that cause learning resources searching, reuse and sharing to become ineffective. In this paper, we present a content-based approach which automatically generates tags from a learning resource using Part-Of-Speech Tagging and K-Means Clustering techniques. The generated tags are more precise and unambiguous which can improve learning resources searching, reuse and sharing among learners. © 2016 Universiti Putra Malaysia Press.},
  affiliation     = {School of Computing and IT, Taylor’s University, Subang Jaya, Selangor, Malaysia},
  author_keywords = {Automatic tagging; Clustering; Collaborative tagging; Folksonomy; K-means; Learning content reuse; Learning content sharing; Learning process; Part-of-speech},
  document_type   = {Article},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84974697726&partnerID=40&md5=2d94064dac1f9d55d8dd281d72cc6206},
}

@Article{yao_etal_2017,
  author        = {J. Yao and Y. Wang and Y. Zhang and J. Sun and J. Zhou},
  title         = {Joint Latent Dirichlet Allocation for Social Tags},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2017},
  volume        = {PP},
  number        = {99},
  pages         = {1-1},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Social tags serve as a textual source of simple but useful semantic metadata that reflect user preferences or describe web objects and have been widely used in many applications. However, social tags have several unique characteristics, i.e., sparseness and data coupling (i.e. non-IIDness), which means that existing text analysis methods such as LDA are not directly applicable. In this article, we propose a new generative algorithm for social tag analysis named Joint Latent Dirichlet Allocation that models tag generation based on both users and objects and, thus, accounts for the coupling relationships among social tags. The model introduces two latent factors that jointly influence tag generation: the user's latent interest factor and the object's latent topic factor, formulated as a user-topic distribution matrix and an object-topic distribution matrix, respectively. A Gibbs sampling approach is adopted to simultaneously infer the above two matrices as well as a topic-word distribution matrix. Experimental results on four social tagging datasets showed that our model is able to capture more reasonable topics and achieves better performance than five state-of-the-art topic models in terms of the widely-used point-wise mutual information (PMI) metric. In addition, we analyze the learned topics and show that our model recovers more themes from social tags, while LDA may lead to vanishing topic problems. We also demonstrate our model's advantages in social recommendation by evaluating the retrieval results using the Mean Reciprocal Rank (MRR) metric. Finally, we explore the joint procedure of our model in depth to show the non-IID characteristics of the social tagging process.},
  doi           = {10.1109/TMM.2017.2716829},
  keywords      = {Analytical models;Data models;Measurement;Resource management;Semantics;Tagging;Visualization;Joint Latent Dirichlet Allocation;Non-IID learning;Topic models;social tags;structure mining},
}

@Article{zhang_etal_2017,
  author        = {J. Zhang and Y. Yang and Q. Tian and L. Zhuo and X. Liu},
  title         = {Personalized Social Image Recommendation Method Based on User-Image-Tag Model},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2017},
  volume        = {PP},
  number        = {99},
  pages         = {1-1},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {In the social image sharing websites (such as Flickr), users are allowed to upload images and to tag them with tags. Due to the diversities of users’ interests, different users may tag the same image with different tags. Therefore, tags not only reveal some important image semantic clues, but also show user’s preference, which can provide a new effective solution for overcoming the semantic gap as well as realizing a personalized recommendation. In this paper, a personalized social image recommendation method based on User-Image-Tag model is proposed. The main contributions of our work are that: (1) to efficiently make use of tags, social image tags are re-ranked according to the image content; (2) to obtain user preference, a User-Image-Tag model is constructed with tripartite graph according to the correlation among users, images and Topranking tags; (3) a personalized social recommendation system is implemented based on User-Image-Tag model. Experimental results proved our method can significantly improve the accuracy of personalized image recommendation.},
  doi           = {10.1109/TMM.2017.2701641},
  keywords      = {Electronic mail;Feature extraction;Media;Multimedia communication;Semantics;Visualization;Vocabulary;Social image;User-Image-Tag model;personalized recommendation;tag ranking;tripartite graph},
}

@Article{li_tang_2017,
  author        = {Z. Li and J. Tang},
  title         = {Weakly Supervised Deep Matrix Factorization for Social Image Understanding},
  journal       = {IEEE Transactions on Image Processing},
  year          = {2017},
  volume        = {26},
  number        = {1},
  pages         = {276-288},
  month         = {Jan},
  issn          = {1057-7149},
  __markedentry = {[felipe:1]},
  abstract      = {The number of images associated with weakly supervised user-provided tags has increased dramatically in recent years. User-provided tags are incomplete, subjective and noisy. In this paper, we focus on the problem of social image understanding, i.e., tag refinement, tag assignment, and image retrieval. Different from previous work, we propose a novel weakly supervised deep matrix factorization algorithm, which uncovers the latent image representations and tag representations embedded in the latent subspace by collaboratively exploring the weakly supervised tagging information, the visual structure, and the semantic structure. Due to the well-known semantic gap, the hidden representations of images are learned by a hierarchical model, which are progressively transformed from the visual feature space. It can naturally embed new images into the subspace using the learned deep architecture. The semantic and visual structures are jointly incorporated to learn a semantic subspace without overfitting the noisy, incomplete, or subjective tags. Besides, to remove the noisy or redundant visual features, a sparse model is imposed on the transformation matrix of the first layer in the deep architecture. Finally, a unified optimization problem with a well-defined objective function is developed to formulate the proposed problem and solved by a gradient descent procedure with curvilinear search. Extensive experiments on real-world social image databases are conducted on the tasks of image understanding: image tag refinement, assignment, and retrieval. Encouraging results are achieved with comparison with the state-of-the-art algorithms, which demonstrates the effectiveness of the proposed method.},
  doi           = {10.1109/TIP.2016.2624140},
  keywords      = {gradient methods;image representation;image retrieval;matrix decomposition;optimisation;search problems;semantic networks;social networking (online);WDMF algorithm;curvilinear search;deep architecture;gradient descent procedure;image representation;image retrieval;objective function;semantic structure;social image understanding;tag assignment;tag refinement;tag representation;unified optimization problem;visual structure;weakly supervised deep matrix factorization;Data models;Image retrieval;Noise measurement;Semantics;Sparse matrices;Tagging;Visualization;Deep architecture;image retrieval;image tagging;matrix factorization;weakly supervised},
}

@InProceedings{zhou_etal_2017,
  author        = {P. Zhou and J. Liu and Z. Yang and G. Zhou},
  title         = {Scalable tag recommendation for software information sites},
  booktitle     = {2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER)},
  year          = {2017},
  pages         = {272-282},
  month         = {Feb},
  __markedentry = {[felipe:1]},
  abstract      = {Software developers can search, share and learn development experience, solutions, bug fixes and open source projects in software information sites such as StackOverflow and Freecode. Many software information sites rely on tags to classify their contents, i.e. software objects, in order to improve the performance and accuracy of various operations on the sites. The quality of tags thus has a significant impact on the usefulness of these sites. High quality tags are expected to be concise and can describe the most important features of the software objects. Unfortunately tagging is inherently an uncoordinated process. The choice of tags made by individual software developers is dependent not only on a developer's understanding of the software object but also on the developer's English skills and preferences. As a result, the number of different tags grows rapidly along with continuous addition of software objects. With thousands of different tags, many of which introduce noise, software objects become poorly classified. Such phenomenon affects negatively the speed and accuracy of developers' queries. In this paper, we propose a tool called TagMulRec to automatically recommend tags and classify software objects in evolving large-scale software information sites. Given a new software object, TagMulRec locates the software objects that are semantically similar to the new one and exploit their tags. We have evaluated TagMulRec on four software information sites, StackOverflow, AskUbuntu, AskDifferent and Freecode. According to our empirical study, TagMulRec is not only accurate but also scalable that can handle a large-scale software information site with millions of software objects and thousands of tags.},
  doi           = {10.1109/SANER.2017.7884628},
  keywords      = {program debugging;public domain software;recommender systems;software engineering;AskDifferent;AskUbuntu;English skills;Freecode;StackOverflow;TagMulRec;bug fixes;development experience;large-scale software information sites;open source projects;scalable tag recommendation;software developers;software objects;Indexes;Search problems;Semantics;Social network services;Software;Software algorithms;Tagging;Multi-Classification;Software Information Site;Software Object;Tag Recommendation, rank1},
}

@Article{cui_etal_2017a,
  author        = {P. Cui and S. Liu and W. Zhu},
  title         = {General Knowledge Embedded Image Representation Learning},
  journal       = {IEEE Transactions on Multimedia},
  year          = {2017},
  volume        = {PP},
  number        = {99},
  pages         = {1-1},
  issn          = {1520-9210},
  __markedentry = {[felipe:1]},
  abstract      = {Image representation learning is a fundamental problem in understanding semantics of images. However, traditional classification-based representation learning methods face the noisy and incomplete problem of the supervisory labels. In this paper, we propose a General Knowledge Base Embedded Image Representation Learning approach, which uses general knowledge graph, which is a multi-type relational knowledge graph consisting of human commonsense beyond image space, as external semantic resource to capture the relations of concepts in image representation learning. A Relational Regularized Regression CNN (R$^{3}$CNN) model is designed to jointly optimize the image representation learning problem and knowledge graph embedding problem. In this manner, the learnt representation can capture not only labeled tags but also related concepts of images, which involves more precise and complete semantics. Comprehensive experiments are conducted to investigate the effectiveness and transferability of our approach in tag prediction task, zero-shot tag inference task, and content based image retrieval task. The experimental results demonstrate that the proposed approach performs significantly better than the existing representation learning methods. Finally, observation of the learnt relations show that our approach can somehow refine the knowledge base to describe images and label the images with structured tags.},
  doi           = {10.1109/TMM.2017.2724843},
  keywords      = {Bridges;Image representation;Knowledge based systems;Learning systems;Noise measurement;Semantics;Visualization;image representation learning;knowledge base;multi-relational graph embedding},
}

@Article{xu_etal_2017,
  author        = {X. Xu and L. He and H. Lu and A. Shimada and R. I. Taniguchi},
  title         = {Non-Linear Matrix Completion for Social Image Tagging},
  journal       = {IEEE Access},
  year          = {2017},
  volume        = {5},
  pages         = {6688-6696},
  issn          = {2169-3536},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper, we address the problem of social image tagging using practical vocabulary for mobile users on the social media. On the social media, images usually have an incomplete or noisy set of social tags provided by the mobile users, and we consider this issue as defective tag assignments. Previous studies on social image tagging have mostly focused on multi-label classification without considering the defective tags. In these studies, the usage of multi-label classification techniques is expected to synergically exploit the linear relations between the image features and the semantic tags. However, these approaches usually aimed to capture the linear relations from the training data while ignoring the helpful information from the test data. In addition, they failed to incorporate the non-linear associations residing in the visual features as well as in the semantic tags. To overcome these drawbacks, we introduce a novel approach based on non-linear matrix completion for image tagging task with defective tags. Specifically, we first construct the entire feature-tag matrix based on the visual features with non-linear kernel mapping. Then, we present a formal methodology together with an optimization method under the matrix completion framework to jointly complete the tags of training and test images. Experimental evaluations demonstrate that our method shows promising results on image tagging task on two benchmark social image datasets with defective tags, and establishes a baseline for such models in this research domain.},
  doi           = {10.1109/ACCESS.2016.2624267},
  keywords      = {Noise measurement;Semantics;Social network services;Sparse matrices;Tagging;Training;Visualization;Social image tagging;defective tag assignment;tag completion, rank1},
}

@InProceedings{huang_etal_2017,
  author        = {Q. Huang and Y. Xu and P. J. B. Jackson and W. Wang and M. D. Plumbley},
  title         = {Fast tagging of natural sounds using marginal co-regularization},
  booktitle     = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year          = {2017},
  pages         = {2991-2995},
  month         = {March},
  __markedentry = {[felipe:1]},
  abstract      = {Automatic and fast tagging of natural sounds in audio collections is a very challenging task due to wide acoustic variations, the large number of possible tags, the incomplete and ambiguous tags provided by different labellers. To handle these problems, we use a co-regularization approach to learn a pair of classifiers on sound and text. The first classifier maps low-level audio features to a true tag list. The second classifier maps actively corrupted tags to the true tags, reducing incorrect mappings caused by low-level acoustic variations in the first classifier, and to augment the tags with additional relevant tags. Training the classifiers is implemented using marginal co-regularization, pair of which draws the two classifiers into agreement by a joint optimization. We evaluate this approach on two sound datasets, Freefield1010 and Task4 of DCASE2016. The results obtained show that marginal co-regularization outperforms the baseline GMM in both efficiency and effectiveness.},
  doi           = {10.1109/ICASSP.2017.7952705},
  keywords      = {audio signal processing;feature extraction;DCASE2016 dataset;Freefield1010 dataset;audio collections;co-regularization approach;low-level acoustic variations;low-level audio features;marginal co-regularization;natural sound tagging;Acoustics;Audio recording;Optimization;Prediction algorithms;Tagging;Training;annotation;co-regularization;natural sound, rank1},
}

@Article{sun_etal_2017,
  author        = {M. Sun and K. H. Zeng and Y. C. Lin and A. Farhadi},
  title         = {Semantic Highlight Retrieval and Term Prediction},
  journal       = {IEEE Transactions on Image Processing},
  year          = {2017},
  volume        = {26},
  number        = {7},
  pages         = {3303-3316},
  month         = {July},
  issn          = {1057-7149},
  __markedentry = {[felipe:1]},
  abstract      = {Due to the unprecedented growth of unedited videos, finding highlights relevant to a text query in a set of unedited videos has become increasingly important. We refer this task as semantic highlight retrieval and propose a query-dependent video representation for retrieving a variety of highlights. Our method consists of two parts: 1) “viralets”, a mid-level representation bridging between semantic [Fig. 1(a)] and visual [Fig. 1(c)] spaces and 2) a novel Semantic-MODulation (SMOD) procedure to make viralets query-dependent (referred to as SMOD viralets). Given SMOD viralets, we train a single highlight ranker to predict the highlightness of clips with respect to a variety of queries (two examples in Fig. 1), whereas existing approaches can be applied only in a few predefined domains. Other than semantic highlight retrieval, viralets can also be used to associate relevant terms to each video. We utilize this property and propose a simple term prediction method based on nearest neighbor search. To conduct experiments, we collect a viral video dataset1 including users’ comments, highlights, and/or original videos. Among a testing database with 1189 clips (13% highlights and 87% non-highlights), our highlight ranker achieves 41.2% recall at top-10 retrieved clips. It is significantly higher than the state-of-the-art domain-specific highlight ranker and its extension. Similarly, our method also outperforms all baseline methods on the publicly available video highlight dataset. Finally, our simple term prediction method utilizing viralets outperforms the state-of-the-art matrix factorization method (adapted from Kalayeh et al.). 1

Viral videos refer to popular online videos. We focus on user-generated viral videos, which typically contain short highlight marked by users.
},
  doi           = {10.1109/TIP.2017.2666039},
  keywords      = {Databases;Nearest neighbor searches;Prediction methods;Semantics;Tagging;Videos;Visualization;Video summarization;highlight retrieval},
}

@InProceedings{yuan_2017,
  author        = {H. Yuan},
  title         = {A Method of Personalized Tag Prediction Based on Graph Structure},
  booktitle     = {2017 IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)},
  year          = {2017},
  pages         = {390-395},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {In the past few years, social tags and tagging systems have gained large momentum for service categorizing and indexing content on the Web. Tags are used freely, which leads a random correspondence between the tags and the services, which affects the performance of the tag in the search and applications. We propose a novel scheme for tag predicting based on graph, aiming to automatically sort the tags associated with a given API services according to their relevance to the service content using the theory of random walk. Tag sorting is applied to tag predicting of Web service. We evaluate our methodology through experiment using open data set. Our results show that our approach really boosts the performances of tag predicting.},
  doi           = {10.1109/QRS-C.2017.71},
  keywords      = {Correlation;Correlation coefficient;Prediction algorithms;Semantics;Sorting;Tagging;Web services;graph;predicting;random walk;service;tag, rank1},
}

@Article{zhang_etal_2017a,
  author        = {J. Zhang and V. S. Sheng and T. Li and X. Wu},
  title         = {Improving Crowdsourced Label Quality Using Noise Correction},
  journal       = {IEEE Transactions on Neural Networks and Learning Systems},
  year          = {2017},
  volume        = {PP},
  number        = {99},
  pages         = {1-14},
  issn          = {2162-237X},
  __markedentry = {[felipe:1]},
  abstract      = {Crowdsourcing systems provide a cost effective and convenient way to collect labels, but they often fail to guarantee the quality of the labels. This paper proposes a novel framework that introduces noise correction techniques to further improve the quality of integrated labels that are inferred from the multiple noisy labels of objects. In the proposed general framework, information about the qualities of labelers estimated by a front-end ground truth inference algorithm is utilized to supervise subsequent label noise filtering and correction. The framework uses a novel algorithm termed adaptive voting noise correction (AVNC) to precisely identify and correct the potential noisy labels. After filtering out the instances with noisy labels, the remaining cleansed data set is used to create multiple weak classifiers, based on which a powerful ensemble classifier is induced to correct these noises. Experimental results on eight simulated data sets with different kinds of features and two real-world crowdsourcing data sets in different domains consistently show that: 1) the proposed framework can improve label quality regardless of inference algorithms, especially under the circumstance that each instance has a few repeated labels and 2) since the proposed AVNC algorithm considers both the number of and the probability of potential label noises, it outperforms the state-of-the-art noise correction algorithms.},
  doi           = {10.1109/TNNLS.2017.2677468},
  keywords      = {Algorithm design and analysis;Crowdsourcing;Inference algorithms;Labeling;Noise measurement;Prediction algorithms;Predictive models;Crowdsourcing;ground truth inference;label integration;label noise correction;label quality.},
}

@Article{wang_etal_2017,
  author        = {Peng Wang and Lifeng Sun and Shiqiang Yang and Alan F. Smeaton},
  title         = {Training-free indexing refinement for visual media via multi-semantics},
  journal       = {Neurocomputing},
  year          = {2017},
  volume        = {236},
  pages         = {39 - 47},
  issn          = {0925-2312},
  note          = {Good Practices in Multimedia Modeling},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2016.08.107},
  keywords      = {Semantic indexing, Refinement, Concept detection enhancement, Context fusion, Factorization, Propagation},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231216313844},
}

@Article{barezi_etal_2017,
  author        = {Elham J. Barezi and James T. Kwok and Hamid R. Rabiee},
  title         = {Multi-Label learning in the independent label sub-spaces},
  journal       = {Pattern Recognition Letters},
  year          = {2017},
  volume        = {97},
  pages         = {8 - 12},
  issn          = {0167-8655},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.patrec.2017.06.024},
  keywords      = {Large scale learning, Multi label learning, Independent learning, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S0167865517302295},
}

@Article{cui_etal_2017,
  author        = {Chaoran Cui and Peiguang Lin and Xiushan Nie and Yilong Yin and Qingfeng Zhu},
  title         = {Hybrid textual-visual relevance learning for content-based image retrieval},
  journal       = {Journal of Visual Communication and Image Representation},
  year          = {2017},
  volume        = {48},
  pages         = {367 - 374},
  issn          = {1047-3203},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.jvcir.2017.03.011},
  keywords      = {Content-based image retrieval, Tag completion, Semantics modeling, Rank aggregation, Sparse linear method},
  url           = {http://www.sciencedirect.com/science/article/pii/S1047320317300731},
}

@Article{maihami_yaghmaee_2017,
  author        = {Vafa Maihami and Farzin Yaghmaee},
  title         = {A genetic-based prototyping for automatic image annotation},
  journal       = {Computers \& Electrical Engineering},
  year          = {2017},
  issn          = {0045-7906},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.compeleceng.2017.03.019},
  keywords      = {Automatic image annotation, Genetic algorithm, Prototyping, Relevance tags, Image retrieval},
  url           = {http://www.sciencedirect.com/science/article/pii/S0045790617306407},
}

@Article{foley_etal_2017,
  author          = {Foley, J. and Kwan, P. and Welch, M.},
  title           = {A Web-Based Infrastructure for the Assisted Annotation of Heritage Collections},
  journal         = {Journal on Computing and Cultural Heritage},
  year            = {2017},
  volume          = {10},
  number          = {3},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Annotations provide a valuable perspective on the semantic information present in digital heritage collections, and in recent years they've been employed in a number of innovative, user-centric techniques that can personalise a user's experience of heritage materials, such as by actively adapting exhibits as a user reveals their interests, or by guiding users to explore collections which are meaningfully linked to what they have previously encountered. Despite the captivating opportunities offered by these techniques, collecting annotations for a large heritage collection is no trivial task. A significant amount of work is required to manually annotate large quantities of heritage materials, and automated, computational approaches leave much to be desired regarding the level of insight and semantic richness that they can currently provide. By analysing the emergent relationships between the initial annotations in a collection, we propose a metadata-driven algorithm for assisting and augmenting the annotation process. This algorithm, called SAGA (Semantically-Annotated Graph Analysis), allows for semi-automatic annotation, which balances the value of the contributions of human annotators with the time and effort-saving benefits of an automatic, suggestion-driven process. SAGA uses an entity relationship-driven approach to make annotation suggestions. It is used in the context of a web-based infrastructure called SAGE (Semantic Annotation by Group Exploration), a multiagent environment which assists groups of experts in creating comprehensive annotation sets for heritage collections. SAGA and SAGE are evaluated from the perspectives of suggestion accuracy, explicit user acceptance and implicit user acceptance, and demonstrate strong results in each evaluation. © 2017 ACM.},
  affiliation     = {School of Science and Technology, University of New England, Armidale, NSW, Australia},
  art_number      = {14},
  author_keywords = {Collaborative tagging; Digital heritage; Tag co-occurrence},
  document_type   = {Article},
  doi             = {10.1145/3012287},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=3129277.3012287},
}

@Article{gong_etal_2017,
  author        = {Yeyun Gong and Qi Zhang and Xuanjing Huang},
  title         = {Hashtag recommendation for multimodal microblog posts},
  journal       = {Neurocomputing},
  year          = {2017},
  issn          = {0925-2312},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2017.06.056},
  keywords      = {Hashtag recommendation, Topical model, Social media},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231217311840},
}

@InCollection{chatterjee_2017,
  author        = {Amitabha Chatterjee},
  title         = {Chapter Y - Automated Information Organization},
  booktitle     = {Elements of Information Organization and Dissemination},
  publisher     = {Chandos Publishing},
  year          = {2017},
  editor        = {Chatterjee, Amitabha},
  pages         = {497 - 526},
  isbn          = {978-0-08-102025-8},
  __markedentry = {[felipe:5]},
  abstract      = {Abstract Information and communication technology is now being increasingly employed in libraries and information centers for improving organization and dissemination of information. Many of the activities, which were earlier being done manually, have now been automated. Besides, a sizeable amount of information sources are now available in electronic or digital format, and Internet has become an important source of information. Not only the existing libraries and their services are now being automated, libraries of new varieties, such as digital libraries and virtual libraries are also coming up. The main areas where efforts are being made to apply computers for increasing the efficiency and speed in the field of information organization are indexing; abstracting; information retrieval tools like thesaurus, ontology, folksonomy, and folksontology; translation; and language processing. These efforts have been briefly discussed in this chapter. },
  doi           = {https://doi.org/10.1016/B978-0-08-102025-8.00025-9},
  keywords      = {Auto-indexing, auto-abstracting, ontology, folksonomy, folksontology, machine translation, natural language processing},
  url           = {http://www.sciencedirect.com/science/article/pii/B9780081020258000259},
}

@Article{xu_etal_2006,
  author        = {Zhichen Xu and Yun Fu and Jianchang Mao and Difu Su},
  title         = {Towards the Semantic Web: Collaborative Tag Suggestions},
  journal       = {Collaborative Web Tagging Workshop on Fifteenth International World Wide Web Conference},
  year          = {2006},
  __markedentry = {[felipe:1]},
  keywords      = {rank2},
  url           = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.97.4194&rep=rep1&type=pdf},
}

@InProceedings{brooks_montanez_2006,
  author        = {Brooks, Christopher H. and Montanez, Nancy},
  title         = {Improved Annotation of the Blogosphere via Autotagging and Hierarchical Clustering},
  booktitle     = {Proceedings of the 15th International Conference on World Wide Web},
  year          = {2006},
  series        = {WWW '06},
  pages         = {625--632},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1135869},
  doi           = {10.1145/1135777.1135869},
  isbn          = {1-59593-323-9},
  keywords      = {automated annotation, blogs, hierarchical clustering, tagging, rank2},
  location      = {Edinburgh, Scotland},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1135777.1135869},
}

@Article{benz_etal_2006,
  author        = {Benz, Dominik and Karen Tso and Lars Schimidt-Thieme},
  title         = {Automatic bookmark classification: A collaborative approach},
  year          = {2006},
  __markedentry = {[felipe:1]},
  keywords      = {rank1},
  url           = {http://www.ra.ethz.ch/CDStore/www2006/www.wmin.ac.uk/~courtes/iwi2006/benz_automatic.pdf},
}

@InProceedings{mishne_2006,
  author        = {Mishne, Gilad},
  title         = {AutoTag: A Collaborative Approach to Automated Tag Assignment for Weblog Posts},
  booktitle     = {Proceedings of the 15th International Conference on World Wide Web},
  year          = {2006},
  series        = {WWW '06},
  pages         = {953--954},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1135961},
  doi           = {10.1145/1135777.1135961},
  isbn          = {1-59593-323-9},
  keywords      = {blogs, tags, rank1},
  location      = {Edinburgh, Scotland},
  numpages      = {2},
  url           = {http://doi.acm.org/10.1145/1135777.1135961},
}

@InBook{jaeschke_etal_2007,
  pages         = {506--514},
  title         = {Tag Recommendations in Folksonomies},
  publisher     = {Springer Berlin Heidelberg},
  year          = {2007},
  author        = {J{\"a}schke, Robert and Marinho, Leandro and Hotho, Andreas and Schmidt-Thieme, Lars and Stumme, Gerd},
  editor        = {Kok, Joost N. and Koronacki, Jacek and Lopez de Mantaras, Ramon and Matwin, Stan and Mladeni{\v{c}}, Dunja and Skowron, Andrzej},
  address       = {Berlin, Heidelberg},
  isbn          = {978-3-540-74976-9},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging systems allow users to assign keywords---so called ``tags''---to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied.},
  booktitle     = {Knowledge Discovery in Databases: PKDD 2007: 11th European Conference on Principles and Practice of Knowledge Discovery in Databases, Warsaw, Poland, September 17-21, 2007. Proceedings},
  doi           = {10.1007/978-3-540-74976-9_52},
  keywords      = {rank1},
  url           = {https://doi.org/10.1007/978-3-540-74976-9_52},
}

@InProceedings{chirita_etal_2007,
  author        = {Chirita, Paul - Alexandru and Costache, Stefania and Nejdl, Wolfgang and Handschuh, Siegfried},
  title         = {P-TAG: Large Scale Automatic Generation of Personalized Annotation Tags for the Web},
  booktitle     = {Proceedings of the 16th International Conference on World Wide Web},
  year          = {2007},
  series        = {WWW '07},
  pages         = {845--854},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1242686},
  doi           = {10.1145/1242572.1242686},
  isbn          = {978-1-59593-654-7},
  keywords      = {personalization, tagging, user desktop, web annotations},
  location      = {Banff, Alberta, Canada},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1242572.1242686},
}

@InProceedings{katakis_etal_2008,
  author               = {Katakis, Ioannis and Tsoumakas, Grigorios and Vlahavas, Ioannis},
  title                = {Multilabel Text Classification for Automated Tag Suggestion},
  booktitle            = {Proceedings of the ECML/PKDD 2008 Discovery Challenge},
  year                 = {2008},
  __markedentry        = {[felipe:1]},
  abstract             = {The increased popularity of tagging during the last few years can be mainly attributed to its embracing by most of the recently thriving user-centric content publishing and management Web 2.0 applications. However, tagging systems have some limitations that have led researchers to develop methods that assist users in the tagging process, by automatically suggesting an appropriate set of tags. We have tried to model the automated tag suggestion problem as a multilabel text classification task in order to participate in the ECML/PKDD 2008 Discovery Challenge.},
  added-at             = {2009-05-26T15:00:29.000+0200},
  biburl               = {http://www.bibsonomy.org/bibtex/23900b844ce31bd81a537081116861a73/lysander07},
  citeulike-article-id = {3790266},
  interhash            = {b9a5f40935e1d4799be3e2fb7db9747d},
  intrahash            = {3900b844ce31bd81a537081116861a73},
  keywords             = {Semantic_Web folksonomy tagging, rank2},
  location             = {Antwerp, Belgium},
  posted-at            = {2008-12-15 21:05:09},
  timestamp            = {2009-06-02T11:03:17.000+0200},
  url                  = {http://lpis.csd.auth.gr/publications/katakis_ecmlpkdd08_challenge.pdf},
}

@InProceedings{heymann_etal_2008,
  author        = {Heymann, Paul and Ramage, Daniel and Garcia-Molina, Hector},
  title         = {Social Tag Prediction},
  booktitle     = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year          = {2008},
  series        = {SIGIR '08},
  pages         = {531--538},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1390425},
  doi           = {10.1145/1390334.1390425},
  isbn          = {978-1-60558-164-4},
  keywords      = {association rules, collaborative tagging, social bookmarking, text classification, rank2},
  location      = {Singapore, Singapore},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1390334.1390425},
}

@InProceedings{sigurbjoernsson_zwol_2008,
  author        = {Sigurbj\"{o}rnsson, B\"{o}rkur and van Zwol, Roelof},
  title         = {Flickr Tag Recommendation Based on Collective Knowledge},
  booktitle     = {Proceedings of the 17th International Conference on World Wide Web},
  year          = {2008},
  series        = {WWW '08},
  pages         = {327--336},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1367542},
  doi           = {10.1145/1367497.1367542},
  isbn          = {978-1-60558-085-2},
  keywords      = {aggregated tag suggestion, collective knowledge, flickr, photo annotations, tag characterisation, tag co-occurence, tag recommendation, rank2},
  location      = {Beijing, China},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1367497.1367542},
}

@InProceedings{song_etal_2008,
  author        = {Song, Yang and Zhuang, Ziming and Li, Huajing and Zhao, Qiankun and Li, Jia and Lee, Wang-Chien and Giles, C. Lee},
  title         = {Real-time Automatic Tag Recommendation},
  booktitle     = {Proceedings of the 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year          = {2008},
  series        = {SIGIR '08},
  pages         = {515--522},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1390423},
  doi           = {10.1145/1390334.1390423},
  isbn          = {978-1-60558-164-4},
  keywords      = {graph partitioning, mixture model, tagging system, rank2},
  location      = {Singapore, Singapore},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1390334.1390423},
}

@InProceedings{sood_etal_2007,
  author        = {Sood, S.C. and Hammond, K.J. and Owsley, S.H. and Birnbaum, L.},
  title         = {{TagAssist: Automatic Tag Suggestion for Blog Posts}},
  booktitle     = {Proceedings of the International Conference on Weblogs and Social Media (ICWSM 2007)},
  year          = {2007},
  __markedentry = {[felipe:1]},
  added-at      = {2007-07-31T14:56:42.000+0200},
  biburl        = {https://www.bibsonomy.org/bibtex/23b79c5c65bb951fb473d02b9be8db272/dolefulrabbit},
  interhash     = {1a946aea1d46f79ad85c25add991bef6},
  intrahash     = {3b79c5c65bb951fb473d02b9be8db272},
  keywords      = {blog suggestion tag tagging, rank1},
  timestamp     = {2007-07-31T14:56:42.000+0200},
  url           = {http://icwsm.org/papers/2--Sood-Owsley-Hammond-Birnbaum.pdf},
}

@Conference{sordo_etal_2007,
  author        = {Sordo, M. and Laurier, C. and Celma, {\`O}.},
  title         = {Annotating Music Collections How content-based similarity helps to propagate labels},
  booktitle     = {8th International Conference on Music Information Retrieval},
  year          = {2007},
  address       = {Vienna, Austria},
  __markedentry = {[felipe:1]},
  abstract      = {In this paper we present a way to annotate music collections by exploiting audio similarity. In this sense, similarity is used to propose labels (tags) to yet unlabeled songs, based on the content-based distance between them. The main goal of our work is to ease the process of annotating huge music collections, by using content-based similarity distances as a way to propagate labels among songs. <br />
<br />
We present two different experiments. The first one propagates labels that are related with the style of the piece, whereas the second experiment deals with mood labels. On the one hand, our approach shows that using a music collection annotated at 40\% with styles, and using content-based, the collection can be automatically annotated up to 78\% (that is, 40\% already annotated and the rest, 38\%, only using propagation), with a recall greater than 0.4. On the other hand, for a smaller music collection annotated at 30\% with moods, the collection can be automatically annotated up to 65\% (e.g. 30\% plus 35\% using propagation).
},
  keywords      = {rank1},
  url           = {http://mtg.upf.edu/files/publications/7c086c-ISMIR-2007-msordo-claurier.pdf},
}

@INPROCEEDINGS{moxley_etal_2008, 
author={E. Moxley and T. Mei and X. S. Hua and W. Y. Ma and B. S. Manjunath}, 
booktitle={2008 IEEE International Conference on Multimedia and Expo}, 
title={Automatic video annotation through search and mining}, 
year={2008}, 
volume={}, 
number={}, 
pages={685-688}, 
keywords={data mining;video retrieval;video signal processing;multimodal search;query keywords;speech-recognized transcripts;supervised identification;unsupervised annotation;unsupervised automatic video annotation;video mining;video query;video search;visual content transcripts;Asia;Automatic speech recognition;Data mining;Image databases;Machine learning;Noise figure;Support vector machines;Tagging;Video sharing;Vocabulary;data mining;video annotation;video search}, 
doi={10.1109/ICME.2008.4607527}, 
ISSN={1945-7871}, 
month={June},}

@InProceedings{chen_etal_2008,
  author        = {Chen, Hong-Ming and Chang, Ming-Hsiu and Chang, Ping-Chieh and Tien, Ming-Chun and Hsu, Winston H. and Wu, Ja-Ling},
  title         = {SheepDog: Group and Tag Recommendation for Flickr Photos by Automatic Search-based Learning},
  booktitle     = {Proceedings of the 16th ACM International Conference on Multimedia},
  year          = {2008},
  series        = {MM '08},
  pages         = {737--740},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1459473},
  doi           = {10.1145/1459359.1459473},
  isbn          = {978-1-60558-303-7},
  keywords      = {concept detection, flickr, recommendation system},
  location      = {Vancouver, British Columbia, Canada},
  numpages      = {4},
  url           = {http://doi.acm.org/10.1145/1459359.1459473},
}

@InProceedings{garg_weber_2008,
  author        = {Garg, Nikhil and Weber, Ingmar},
  title         = {Personalized, Interactive Tag Recommendation for Flickr},
  booktitle     = {Proceedings of the 2008 ACM Conference on Recommender Systems},
  year          = {2008},
  series        = {RecSys '08},
  pages         = {67--74},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1454020},
  doi           = {10.1145/1454008.1454020},
  isbn          = {978-1-60558-093-7},
  keywords      = {flickr, tag co-occurrence, tag recommendation, tagging systems, rank1},
  location      = {Lausanne, Switzerland},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1454008.1454020},
}

@InProceedings{lipczak_2008,
  author        = {Marek Lipczak},
  title         = {Tag Recommendation for Folksonomies Oriented towards Individual Users},
  year          = {2008},
  __markedentry = {[felipe:1]},
  keywords      = {rank1},
  url           = {https://www.kde.cs.uni-kassel.de/ws/rsdc08/pdf/10.pdf},
}

@Article{bertin-mahieux_etal_2008,
  author        = {Thierry Bertin-Mahieux and Douglas Eck and François Maillet and Paul Lamere},
  title         = {Autotagger: A Model for Predicting Social Tags from Acoustic Features on Large Music Databases},
  journal       = {Journal of New Music Research},
  year          = {2008},
  volume        = {37},
  number        = {2},
  pages         = {115-135},
  __markedentry = {[felipe:1]},
  abstract      = { Abstract Social tags are user-generated keywords associated with some resource on the Web. In the case of music, social tags have become an important component of “Web 2.0” recommender systems, allowing users to generate playlists based on use-dependent terms such as chill or jogging that have been applied to particular songs. In this paper, we propose a method for predicting these social tags directly from MP3 files. Using a set of 360 classifiers trained using the online ensemble learning algorithm FilterBoost, we map audio features onto social tags collected from the Web. The resulting automatic tags (or autotags) furnish information about music that is otherwise untagged or poorly tagged, allowing for insertion of previously unheard music into a social recommender. This avoids the “cold-start problem” common in such systems. Autotags can also be used to smooth the tag space from which similarities and recommendations are made by providing a set of comparable baseline tags for all tracks in a recommender system. Because the words we learn are the same as those used by people who label their music collections, it is easy to integrate our predictions into existing similarity and prediction methods based on web data. },
  doi           = {10.1080/09298210802479250},
  eprint        = {http://dx.doi.org/10.1080/09298210802479250},
  keywords      = {rank1},
  url           = {http://www.iro.umontreal.ca/~eckdoug/papers/2008_jnmr.pdf
    
},
}

@Article{mrosek_etal_2009,
  author        = {Johannes Mrosek and Stefan Bussmann and Hendrik Albers and Kai Posdziech and Benedikt Hengefeld},
  title         = {Content- and Graph-based Tag Recommendation: Two Variations},
  year          = {2009},
  __markedentry = {[felipe:1]},
  url           = {https://www.kde.cs.uni-kassel.de/ws/dc09/papers/paper_18.pdf},
}

@Article{ju_hwang_2009,
  author        = {Sanghun Ju and Kyu-Baek Hwang},
  title         = {A Weighting Scheme for Tag Recommendation in Social Bookmarking Systems},
  year          = {2009},
  __markedentry = {[felipe:1]},
  url           = {https://www.kde.cs.uni-kassel.de/ws/dc09/papers/paper_05.pdf},
}

@InProceedings{rendle_schmidt-thieme_2009,
  author        = {Rendle, Steffen and Schmidt-Thieme, Lars},
  title         = {Factor Models for Tag Recommendation in Bibsonomy},
  booktitle     = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year          = {2009},
  series        = {ECMLPKDDDC'09},
  pages         = {235--242},
  address       = {Aachen, Germany, Germany},
  publisher     = {CEUR-WS.org},
  __markedentry = {[felipe:1]},
  acmid         = {3056166},
  keywords      = {rank1},
  location      = {Bled, Slovenia},
  numpages      = {8},
  url           = {https://www.kde.cs.uni-kassel.de/ws/dc09/papers/paper_13.pdf},
}

@Misc{landia_anand_2009,
  author        = {Nikolas Landia and Sarabjot Singh Anand},
  title         = {Personalised Tag Recommendation},
  year          = {2009},
  __markedentry = {[felipe:1]},
  url           = {ceur-ws.org/Vol-532/paper13.pdf},
}

@InProceedings{rendle_etal_2009,
  author        = {Rendle, Steffen and Balby Marinho, Leandro and Nanopoulos, Alexandros and Schmidt-Thieme, Lars},
  title         = {Learning Optimal Ranking with Tensor Factorization for Tag Recommendation},
  booktitle     = {Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year          = {2009},
  series        = {KDD '09},
  pages         = {727--736},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1557100},
  doi           = {10.1145/1557019.1557100},
  isbn          = {978-1-60558-495-9},
  keywords      = {ranking, tag recommendation, tensor factorization, rank2},
  location      = {Paris, France},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1557019.1557100},
}

@InProceedings{krestel_etal_2009,
  author        = {Krestel, Ralf and Fankhauser, Peter and Nejdl, Wolfgang},
  title         = {Latent Dirichlet Allocation for Tag Recommendation},
  booktitle     = {Proceedings of the Third ACM Conference on Recommender Systems},
  year          = {2009},
  series        = {RecSys '09},
  pages         = {61--68},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1639726},
  doi           = {10.1145/1639714.1639726},
  isbn          = {978-1-60558-435-5},
  keywords      = {delicious, social bookmarking system, tag recommendation, tag search, rank2},
  location      = {New York, New York, USA},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1639714.1639726},
}

@InProceedings{liu_etal_2009,
  author        = {Liu, Dong and Hua, Xian-Sheng and Yang, Linjun and Wang, Meng and Zhang, Hong-Jiang},
  title         = {Tag Ranking},
  booktitle     = {Proceedings of the 18th International Conference on World Wide Web},
  year          = {2009},
  series        = {WWW '09},
  pages         = {351--360},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1526757},
  doi           = {10.1145/1526709.1526757},
  isbn          = {978-1-60558-487-4},
  keywords      = {flickr, random walk, recommendation, search, tag ranking},
  location      = {Madrid, Spain},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1526709.1526757},
}

@InProceedings{hassan_etal_2009,
  author        = {Hassan, Malik Tahir and Karim, Asim and Manandhar, Suresh and Cussens, James},
  title         = {Discriminative Clustering for Content-based Tag Recommendation in Social Bookmarking Systems},
  booktitle     = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year          = {2009},
  series        = {ECMLPKDDDC'09},
  pages         = {85--97},
  address       = {Aachen, Germany, Germany},
  publisher     = {CEUR-WS.org},
  __markedentry = {[felipe:1]},
  acmid         = {3056154},
  location      = {Bled, Slovenia},
  numpages      = {13},
  url           = {http://ceur-ws.org/Vol-497/paper_24.pdf},
}

@InProceedings{falleri_etal_2010,
  author        = {Falleri, Jean-R{\'e}my and Azmeh, Zeina and Huchard, Marianne and Tibermacine, Chouki},
  title         = {Automatic tag identification in web service descriptions},
  booktitle     = {WEBIST'10: The International Conference on Web Information Systems and Technology},
  year          = {2010},
  pages         = {8},
  __markedentry = {[felipe:1]},
  url           = {https://hal.archives-ouvertes.fr/lirmm-00533070/document},
}

@Article{lee_etal_2010,
  author        = {Sihyoung Lee and Wesley De Neve and Konstantinos N. Plataniotis and Yong Man Ro},
  title         = {MAP-based image tag recommendation using a visual folksonomy},
  journal       = {Pattern Recognition Letters},
  year          = {2010},
  volume        = {31},
  number        = {9},
  pages         = {976 - 982},
  issn          = {0167-8655},
  __markedentry = {[felipe:1]},
  abstract      = {Descriptive tags are needed to enable efficient and effective search in vast collections of images. Tag recommendation represents a trade-off between automatic image annotation techniques and manual tagging. In this letter, we formulate image tag recommendation as a maximum a posteriori (MAP) problem, making use of a visual folksonomy. A folksonomy can be seen as a collaboratively created set of metadata for informal social classification. Our experimental results show that the use of a visual folksonomy for image tag recommendation has two significant benefits, compared to a conventional approach using a limited concept vocabulary. First, our tag recommendation technique can make use of an unrestricted and rich concept vocabulary. Second, our approach is able to recommend a higher number of correct tags.},
  doi           = {http://dx.doi.org/10.1016/j.patrec.2009.12.024},
  keywords      = {Folksonomy, Image annotation, Tagging, Tag recommendation},
  url           = {http://www.sciencedirect.com/science/article/pii/S0167865509003663},
}

@InProceedings{rendle_schmidt-thieme_2010,
  author        = {Rendle, Steffen and Schmidt-Thieme, Lars},
  title         = {Pairwise Interaction Tensor Factorization for Personalized Tag Recommendation},
  booktitle     = {Proceedings of the Third ACM International Conference on Web Search and Data Mining},
  year          = {2010},
  series        = {WSDM '10},
  pages         = {81--90},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1718498},
  doi           = {10.1145/1718487.1718498},
  isbn          = {978-1-60558-889-6},
  keywords      = {personalization, recommender systems, tag recommendation, tensor factorization, rank2},
  location      = {New York, New York, USA},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1718487.1718498},
}

@InProceedings{gemmell_etal_2010,
  author        = {Gemmell, Jonathan and Schimoler, Thomas and Mobasher, Bamshad and Burke, Robin},
  title         = {Hybrid Tag Recommendation for Social Annotation Systems},
  booktitle     = {Proceedings of the 19th ACM International Conference on Information and Knowledge Management},
  year          = {2010},
  series        = {CIKM '10},
  pages         = {829--838},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1871543},
  doi           = {10.1145/1871437.1871543},
  isbn          = {978-1-4503-0099-5},
  keywords      = {hybrid recommendation, information channels, recommender systems, social annotation, rank1},
  location      = {Toronto, ON, Canada},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1871437.1871543},
}

@InBook{preisach_etal_2010,
  pages         = {348--357},
  title         = {Semi-supervised Tag Recommendation - Using Untagged Resources to Mitigate Cold-Start Problems},
  publisher     = {Springer Berlin Heidelberg},
  year          = {2010},
  author        = {Preisach, Christine and Balby Marinho, Leandro and Schmidt-Thieme, Lars},
  editor        = {Zaki, Mohammed J. and Yu, Jeffrey Xu and Ravindran, B. and Pudi, Vikram},
  address       = {Berlin, Heidelberg},
  isbn          = {978-3-642-13657-3},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommender systems are often used in social tagging systems, a popular family of Web 2.0 applications, to assist users in the tagging process. But in cold-start situations i.e., when new users or resources enter the system, state-of-the-art tag recommender systems perform poorly and are not always able to generate recommendations. Many user profiles contain untagged resources, which could provide valuable information especially for cold-start scenarios where tagged data is scarce. The existing methods do not explore this additional information source. In this paper we propose to use a purely graph-based semi-supervised relational approach that uses untagged posts for addressing the cold-start problem. We conduct experiments on two real-life datasets and show that our approach outperforms the state-of-the-art in many cases.},
  booktitle     = {Advances in Knowledge Discovery and Data Mining: 14th Pacific-Asia Conference, PAKDD 2010, Hyderabad, India, June 21-24, 2010. Proceedings. Part I},
  doi           = {10.1007/978-3-642-13657-3_38},
  keywords      = {rank1},
  url           = {https://doi.org/10.1007/978-3-642-13657-3_38},
}

@InProceedings{liu_etal_2011,
  author        = {Liu, Zhiyuan and Chen, Xinxiong and Sun, Maosong},
  title         = {A Simple Word Trigger Method for Social Tag Suggestion},
  booktitle     = {Proceedings of the Conference on Empirical Methods in Natural Language Processing},
  year          = {2011},
  series        = {EMNLP '11},
  pages         = {1577--1588},
  address       = {Stroudsburg, PA, USA},
  publisher     = {Association for Computational Linguistics},
  __markedentry = {[felipe:1]},
  abstract      = {It is popular for users in Web 2.0 era to freely annotate online resources with tags. To ease the annotation process, it has been great interest in automatic tag suggestion. We propose a method to suggest tags according to the text description of a resource. By considering both the description and tags of a given resource as summaries to the resource written in two languages, we adopt word alignment models in statistical machine translation to bridge their vocabulary gap. Based on the translation probabilities between the words in descriptions and the tags estimated on a large set of description-tags pairs, we build a word trigger method (WTM) to suggest tags according to the words in a resource description. Experiments on real world datasets show that WTM is effective and robust compared with other methods. Moreover, WTM is relatively simple and efficient, which is practical for Web applications.},
  acmid         = {2145601},
  isbn          = {978-1-937284-11-4},
  keywords      = {rank1},
  location      = {Edinburgh, United Kingdom},
  numpages      = {12},
  url           = {http://dl.acm.org/citation.cfm?id=2145432.2145601},
}

@Article{song_etal_2011,
  author        = {Song, Yang and Zhang, Lu and Giles, C. Lee},
  title         = {Automatic Tag Recommendation Algorithms for Social Recommender Systems},
  journal       = {ACM Trans. Web},
  year          = {2011},
  volume        = {5},
  number        = {1},
  pages         = {4:1--4:31},
  month         = feb,
  issn          = {1559-1131},
  __markedentry = {[felipe:1]},
  abstract      = {The emergence of Web 2.0 and the consequent success of social network Web sites such as Del.icio.us and Flickr introduce us to a new concept called social bookmarking, or tagging. Tagging is the action of connecting a relevant user-defined keyword to a document, image, or video, which helps the user to better organize and share their collections of interesting stuff. With the rapid growth of Web 2.0, tagged data is becoming more and more abundant on the social network Web sites. An interesting problem is how to automate the process of making tag recommendations to users when a new resource becomes available.

In this article, we address the issue of tag recommendation from a machine learning perspective. From our empirical observation of two large-scale datasets, we first argue that the user-centered approach for tag recommendation is not very effective in practice. Consequently, we propose two novel document-centered approaches that are capable of making effective and efficient tag recommendations in real scenarios. The first, graph-based, method represents the tagged data in two bipartite graphs, (document, tag) and (document, word), then finds document topics by leveraging graph partitioning algorithms. The second, prototype-based, method aims at finding the most representative documents within the data collections and advocates a sparse multiclass Gaussian process classifier for efficient document classification. For both methods, tags are ranked within each topic cluster/class by a novel ranking method. Recommendations are performed by first classifying a new document into one or more topic clusters/classes, and then selecting the most relevant tags from those clusters/classes as machine-recommended tags.

Experiments on real-world data from Del.icio.us, CiteULike, and BibSonomy examine the quality of tag recommendation as well as the efficiency of our recommendation algorithms. The results suggest that our document-centered models can substantially improve the performance of tag recommendations when compared to the user-centered methods, as well as topic models LDA and SVM classifiers.
},
  acmid         = {1921595},
  address       = {New York, NY, USA},
  articleno     = {4},
  doi           = {10.1145/1921591.1921595},
  issue_date    = {February 2011},
  keywords      = {Gaussian processes, Tagging system, graph partitioning, mixture model, multi-label classification, prototype selection, rank2},
  numpages      = {31},
  publisher     = {ACM},
  url           = {http://doi.acm.org/10.1145/1921591.1921595},
}

@InProceedings{cai_etal_2011,
  author        = {Cai, Yuanzhe and Zhang, Miao and Luo, Dijun and Ding, Chris and Chakravarthy, Sharma},
  title         = {Low-order Tensor Decompositions for Social Tagging Recommendation},
  booktitle     = {Proceedings of the Fourth ACM International Conference on Web Search and Data Mining},
  year          = {2011},
  series        = {WSDM '11},
  pages         = {695--704},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1935920},
  doi           = {10.1145/1935826.1935920},
  isbn          = {978-1-4503-0493-1},
  keywords      = {graph mining, recommendation system, social network, rank2},
  location      = {Hong Kong, China},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1935826.1935920},
}

@Article{ramezani_2011,
  author        = {Ramezani, Maryam},
  title         = {Improving graph-based approaches for personalized tag recommendation},
  year          = {2011},
  __markedentry = {[felipe:1]},
  keywords      = {rank2},
  url           = {http://www.jetwi.us/uploadfile/2014/1223/20141223024512711.pdf},
}

@InProceedings{belem_etal_2011,
  author        = {Bel{\'e}m, Fabiano and Martins, Eder and Pontes, Tatiana and Almeida, Jussara and Gon\c{c}alves, Marcos},
  title         = {Associative Tag Recommendation Exploiting Multiple Textual Features},
  booktitle     = {Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year          = {2011},
  series        = {SIGIR '11},
  pages         = {1033--1042},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {2010053},
  doi           = {10.1145/2009916.2010053},
  isbn          = {978-1-4503-0757-4},
  keywords      = {relevance metrics, tag recommendation, rank1},
  location      = {Beijing, China},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/2009916.2010053},
}

@Article{hamouda_wanas_2011,
  author        = {Sally Hamouda and Nayer M. Wanas},
  title         = {PUT-Tag: personalized user-centric tag recommendation for social bookmarking systems},
  journal       = {Social Network Analysis and Mining},
  year          = {2011},
  volume        = {1},
  pages         = {377-385},
  __markedentry = {[felipe:1]},
  url           = {https://link.springer.com/article/10.1007/s13278-011-0028-6},
}

@Article{krestel_fankhauser_2012,
  author        = {Ralf Krestel and Peter Fankhauser},
  title         = {Personalized topic-based tag recommendation},
  journal       = {Neurocomputing},
  year          = {2012},
  volume        = {76},
  number        = {1},
  pages         = {61 - 70},
  issn          = {0925-2312},
  note          = {Seventh International Symposium on Neural Networks (ISNN 2010) Advances in Web Intelligence},
  __markedentry = {[felipe:1]},
  abstract      = {More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences. In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.},
  doi           = {http://dx.doi.org/10.1016/j.neucom.2011.04.034},
  keywords      = {Tag recommendation, Personalization, Language models, Topic models, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S0925231211003985},
}

@InBook{leginus_etal_2012,
  pages         = {151--163},
  title         = {Improving Tensor Based Recommenders with Clustering},
  publisher     = {Springer Berlin Heidelberg},
  year          = {2012},
  author        = {Leginus, Martin and Dolog, Peter and {\v{Z}}emaitis, Valdas},
  editor        = {Masthoff, Judith and Mobasher, Bamshad and Desmarais, Michel C. and Nkambou, Roger},
  address       = {Berlin, Heidelberg},
  isbn          = {978-3-642-31454-4},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging systems (STS) model three types of entities (i.e. tag-user-item) and relationships between them are encoded into a 3-order tensor. Latent relationships and patterns can be discovered by applying tensor factorization techniques like Higher Order Singular Value Decomposition (HOSVD), Canonical Decomposition etc. STS accumulate large amount of sparse data that restricts factorization techniques to detect latent relations and also significantly slows down the process of a factorization. We propose to reduce tag space by exploiting clustering techniques so that the quality of the recommendations and execution time are improved and memory requirements are decreased. The clustering is motivated by the fact that many tags in a tag space are semantically similar thus the tags can be grouped. Finally, promising experimental results are presented.},
  booktitle     = {User Modeling, Adaptation, and Personalization: 20th International Conference, UMAP 2012, Montreal, Canada, July 16-20, 2012. Proceedings},
  doi           = {10.1007/978-3-642-31454-4_13},
  url           = {https://doi.org/10.1007/978-3-642-31454-4_13},
}

@Article{hu_etal_2012,
  author        = {Hu, Jun and Wang, Bing and Liu, Yu and Li, De-Yi},
  title         = {Personalized Tag Recommendation Using Social Influence},
  journal       = {Journal of Computer Science and Technology},
  year          = {2012},
  volume        = {27},
  number        = {3},
  pages         = {527--540},
  month         = {Jan},
  issn          = {1860-4749},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommendation encourages users to add more tags in bridging the semantic gap between human concept and the features of media object, which provides a feasible solution for content-based multimedia information retrieval. In this paper, we study personalized tag recommendation in a popular online photo sharing site --- Flickr. Social relationship information of users is collected to generate an online social network. From the perspective of network topology, we propose node topological potential to characterize user's social influence. With this metric, we distinguish different social relations between users and find out those who really have influence on the target users. Tag recommendations are based on tagging history and the latent personalized preference learned from those who have most influence in user's social network. We evaluate our method on large scale real-world data. The experimental results demonstrate that our method can outperform the non-personalized global co-occurrence method and other two state-of-the-art personalized approaches using social networks. We also analyze the further usage of our approach for the cold-start problem of tag recommendation.},
  day           = {01},
  doi           = {10.1007/s11390-012-1241-0},
  url           = {https://doi.org/10.1007/s11390-012-1241-0},
}

@InProceedings{belem_etal_2013,
  author        = {Bel{\'e}m, Fabiano and Santos, Rodrygo and Almeida, Jussara and Gon\c{c}alves, Marcos},
  title         = {Topic Diversity in Tag Recommendation},
  booktitle     = {Proceedings of the 7th ACM Conference on Recommender Systems},
  year          = {2013},
  series        = {RecSys '13},
  pages         = {141--148},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {2507184},
  doi           = {10.1145/2507157.2507184},
  isbn          = {978-1-4503-2409-0},
  keywords      = {diversity, relevance, tag recommendation},
  location      = {Hong Kong, China},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/2507157.2507184},
}

@Article{chen_shin_2013,
  author        = {Chen, Xian and Shin, Hyoseop},
  title         = {Tag Recommendation by Machine Learning with Textual and Social Features},
  journal       = {J. Intell. Inf. Syst.},
  year          = {2013},
  volume        = {40},
  number        = {2},
  pages         = {261--282},
  month         = apr,
  issn          = {0925-9902},
  __markedentry = {[felipe:1]},
  acmid         = {2457922},
  address       = {Hingham, MA, USA},
  doi           = {10.1007/s10844-012-0200-0},
  issue_date    = {April 2013},
  keywords      = {Flickr, Machine learning, Social features, Social media, Tag recommendation, Textual features},
  numpages      = {22},
  publisher     = {Kluwer Academic Publishers},
  url           = {http://dx.doi.org/10.1007/s10844-012-0200-0},
}

@InProceedings{yagnik_etal_2014,
  author        = {Shweta Yagnik AND Priyank Thakkar and Kotecha k.},
  title         = {Recommending Tags for new Resources in Social Bookmarking Systems},
  booktitle     = {International Journal of Data Mining and Knowledge Management Process},
  year          = {2014},
  __markedentry = {[felipe:1]},
  abstract      = {Social bookmarking system is a web-based resource sharing system that allows users to upload, share and organize  their  resources  i.e.  bookmarks  and  publications.  The  system  has  shifted  the  paradigm  of bookmarking  from  an  individual  activity  limited  to 
desktop  to  a  collective  activity  on  the  web.  It  also facilitates  user  to  annotate  his  resource  with  free  form  tags  that  leads  to  large  communities  of  users to collaboratively  create  accessible  repositories  of  web  resources.  Tagging  process  has  its  own  challenges like  ambiguity,  redundancy  or  misspelled  tags  and  sometimes  user  tends  to  avoid  it  as  he  has  to  describe tag  at  his  own.  The  resultant  tag  space  is  noisy  or  very  sparse  and  dilutes  the  purpose  of  tagging.  The effective  solution  is  Tag  Recommendation  System  that  automatically  suggests  appropriate  set  of  tags  touser  while  annotating  resource.  In  this  paper,  we  propose  a  framework  that  does  not  depend  on  tagging history  of  the  resource  or  user  and  thereby  capable  of  suggesting  tags  to  the  resources  which  are  being submitted  to  the  system  first  time.  We  model  tag  recommendation  task  as  multi-label  text  classification problem and use Naive Bayes classifier as the base learner of the multilabel classifier. We experiment with Boolean,  bag-of-words  and  term  frequency-inverse  document  frequency  (TFIDF)  representation  of  the resources  and  fit  appropriate  distribution  to  the  data  based  on  the  representation  used.  Impact  of  feature selection  on  the  effectiveness  of  the  tag  recommendation  is  also  studied. Effectiveness  of  the  proposed framework is evaluated through precision, recall and f-measure metrics.  },
  keywords      = {rank1},
  url           = {http://aircconline.com/ijdkp/V4N1/4114ijdkp02.pdf},
}

@InProceedings{saito_matsui_2015,
  author        = {Saito, Masaki and Matsui, Yusuke},
  title         = {Illustration2Vec: A Semantic Vector Representation of Illustrations},
  booktitle     = {SIGGRAPH Asia 2015 Technical Briefs},
  year          = {2015},
  series        = {SA '15},
  pages         = {5:1--5:4},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {2820907},
  articleno     = {5},
  doi           = {10.1145/2820903.2820907},
  isbn          = {978-1-4503-3930-8},
  keywords      = {CNNs, illustration, search, visual similarity, rank1},
  location      = {Kobe, Japan},
  numpages      = {4},
  url           = {http://doi.acm.org/10.1145/2820903.2820907},
}

@Article{mazloom_etal_2015,
  author        = {Masoud Mazloom and Xirong Li and Cees G. M. Snoek},
  title         = {TagBook: {A} Semantic Video Representation without Supervision for Event Detection},
  journal       = {CoRR},
  year          = {2015},
  volume        = {abs/1510.02899},
  __markedentry = {[felipe:1]},
  bibsource     = {dblp computer science bibliography, http://dblp.org},
  biburl        = {http://dblp.uni-trier.de/rec/bib/journals/corr/MazloomLS15},
  keywords      = {rank1},
  timestamp     = {Wed, 07 Jun 2017 14:42:37 +0200},
  url           = {http://arxiv.org/abs/1510.02899},
}

@InProceedings{wu_etal_2016,
  author        = {Wu, Yong and Yao, Yuan and Xu, Feng and Tong, Hanghang and Lu, Jian},
  title         = {Tag2Word: Using Tags to Generate Words for Content Based Tag Recommendation},
  booktitle     = {Proceedings of the 25th ACM International on Conference on Information and Knowledge Management},
  year          = {2016},
  series        = {CIKM '16},
  pages         = {2287--2292},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommendation is helpful for the categorization and searching of online content. Existing tag recommendation methods can be divided into collaborative filtering methods and content based methods. In this paper, we put our focus on the content based tag recommendation due to its wider applicability. Our key observation is the tag-content co-occurrence, i.e., many tags have appeared multiple times in the corresponding content. Based on this observation, we propose a generative model (Tag2Word), where we generate the words based on the tag-word distribution as well as the tag itself. Experimental evaluations on real data sets demonstrate that the proposed method outperforms several existing methods in terms of recommendation accuracy, while enjoying linear scalability.},
  acmid         = {2983682},
  doi           = {10.1145/2983323.2983682},
  isbn          = {978-1-4503-4073-1},
  keywords      = {generative model, tag recommendation, tag-content co-occurrence, rank1},
  location      = {Indianapolis, Indiana, USA},
  numpages      = {6},
  url           = {http://doi.acm.org/10.1145/2983323.2983682},
}

@Article{haifeng_etal_2017,
  author        = {Haifeng, Guo and Shoubao, Su and Zhoubao, Sun},
  title         = {Image tag recommendation based on friendships},
  journal       = {Multimedia Tools and Applications},
  year          = {2017},
  volume        = {76},
  number        = {13},
  pages         = {14581--14597},
  month         = {Jul},
  issn          = {1573-7721},
  __markedentry = {[felipe:1]},
  abstract      = {The tagging systems have been studied by many researchers in the past decade. Tagging methods have been widely used on the web for searching and recommending images. Social tags are the keywords annotated by users to the images, which contains the information for searching and classifying the images. Tag recommendation system allows mitigating the individual preferences to annotate and recommender images. However, irrelevant and noise tags are frequently included in tags. In this paper, we propose image tag recommendation based on the friends' relationships in social network (TRboFS) to recommender tags for a new image, both the tags assigned to the favorite images and the friendships of the users who upload the image are employed to predict the tags of the images. Empirical analyses on real datasets show that the proposed approach achieves superior performance to existing approaches.},
  day           = {01},
  doi           = {10.1007/s11042-016-3802-7},
  url           = {https://doi.org/10.1007/s11042-016-3802-7},
}

@Misc{lee_chun_2007,
  author        = {Sigma On Kee Lee and Andy Hon Wai Chun},
  title         = {Automatic Tag Recommendation for the Web 2.0 Blogosphere Using Collaborative Tagging and Hybrid ANN Semantic Structures},
  year          = {2007},
  __markedentry = {[felipe:1]},
  url           = {http://citeseerx.ist.psu.edu/viewdoc/summary;jsessionid=5CFB2F6510207BB4B2AEE44EB3E76CAC?doi=10.1.1.129.2749},
}

@InProceedings{tatu_etal_2008,
  author        = {Marta Tatu and Munirathnam Srikanth and Thomas D’Silva},
  title         = {RSDC’08: Tag Recommendations using Bookmark Content},
  year          = {2008},
  __markedentry = {[felipe:1]},
  url           = {https://www.kde.cs.uni-kassel.de/ws/rsdc08/pdf/11.pdf},
}

@InProceedings{wu_etal_2009,
  author        = {Wu, Lei and Yang, Linjun and Yu, Nenghai and Hua, Xian-Sheng},
  title         = {Learning to Tag},
  booktitle     = {Proceedings of the 18th International Conference on World Wide Web},
  year          = {2009},
  series        = {WWW '09},
  pages         = {361--370},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1526758},
  doi           = {10.1145/1526709.1526758},
  isbn          = {978-1-60558-487-4},
  keywords      = {learning to tag, multi-modality rankboost, social tagging, tag recommendation},
  location      = {Madrid, Spain},
  numpages      = {10},
  url           = {http://doi.acm.org/10.1145/1526709.1526758},
}

@Article{si_sun_2008,
  author        = {Si, Xiance and Sun, Maosong},
  title         = {Tag-LDA for Scalable Real-time Tag Recommendation},
  year          = {2008},
  volume        = {6},
  month         = {11},
  __markedentry = {[felipe:1]},
  booktitle     = {Journal of Computational Information Systems},
  url           = {https://www.yumpu.com/en/document/view/40719286/tag-lda-for-scalable-real-time-tag-recommendation},
}

@InBook{harvey_etal_2010,
  pages         = {432--443},
  title         = {Tripartite Hidden Topic Models for Personalised Tag Suggestion},
  publisher     = {Springer Berlin Heidelberg},
  year          = {2010},
  author        = {Harvey, Morgan and Baillie, Mark and Ruthven, Ian and Carman, Mark J.},
  editor        = {Gurrin, Cathal and He, Yulan and Kazai, Gabriella and Kruschwitz, Udo and Little, Suzanne and Roelleke, Thomas and R{\"u}ger, Stefan and van Rijsbergen, Keith},
  address       = {Berlin, Heidelberg},
  isbn          = {978-3-642-12275-0},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging systems provide methods for users to categorise resources using their own choice of keywords (or ``tags'') without being bound to a restrictive set of predefined terms. Such systems typically provide simple tag recommendations to increase the number of tags assigned to resources. In this paper we extend the latent Dirichlet allocation topic model to include user data and use the estimated probability distributions in order to provide personalised tag suggestions to users. We describe the resulting tripartite topic model in detail and show how it can be utilised to make personalised tag suggestions. Then, using data from a large-scale, real life tagging system, test our system against several baseline methods. Our experiments show a statistically significant increase in performance of our model over all key metrics, indicating that the model could be successfully used to provide further social tagging tools such as resource suggestion and collaborative filtering.},
  booktitle     = {Advances in Information Retrieval: 32nd European Conference on IR Research, ECIR 2010, Milton Keynes, UK, March 28-31, 2010.Proceedings},
  doi           = {10.1007/978-3-642-12275-0_38},
  keywords      = {rank1},
  url           = {https://doi.org/10.1007/978-3-642-12275-0_38},
}

@InProceedings{guan_etal_2009,
  author        = {Guan, Ziyu and Bu, Jiajun and Mei, Qiaozhu and Chen, Chun and Wang, Can},
  title         = {Personalized Tag Recommendation Using Graph-based Ranking on Multi-type Interrelated Objects},
  booktitle     = {Proceedings of the 32Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
  year          = {2009},
  series        = {SIGIR '09},
  pages         = {540--547},
  address       = {New York, NY, USA},
  publisher     = {ACM},
  __markedentry = {[felipe:1]},
  acmid         = {1572034},
  doi           = {10.1145/1571941.1572034},
  isbn          = {978-1-60558-483-6},
  keywords      = {personalization, ranking, recommender systems, social tagging, rank1},
  location      = {Boston, MA, USA},
  numpages      = {8},
  url           = {http://doi.acm.org/10.1145/1571941.1572034},
}

@InProceedings{wetzker_etal_2009,
  author        = {Wetzker, Robert and Said, Alan and Zimmermann2, Carsten},
  title         = {Understanding the user: Personomy translation for tag recommendation},
  booktitle     = {ECML PKDD Discovery Challenge 2009 (DC09)},
  year          = {2009},
  editor        = {Eisterlehner, Folke and Hotho, Andreas and Jäschke, Robert},
  volume        = {497},
  pages         = {275--284},
  address       = {Bled, Slovenia},
  month         = {September},
  publisher     = {CEUR Workshop Proceedings},
  __markedentry = {[felipe:1]},
  abstract      = {This paper describes our approach to the challenge of graphbased tag recommendation in social bookmarking services. Along the ECML PKDD 2009 Discovery Challenge, we design a tag recommender that accurately predicts the tagging behavior of users within the Bibsonomy bookmarking service. We find that the tagging vocabularies among folksonomy users differ radically due to multilingual aspects as well as heterogeneous tagging habits. Our model overcomes the prediction problem resulting from these heterogeneities by translating user vocabularies, so called personomies, to the global folksonomy vocabulary and vice versa. Furthermore we combine our user-centric translation approach with item-centric methods to achieve more accurate solutions. Since our method is purely graph-based, it can also readily be applied to other folksonomies.},
  added-at      = {2010-01-29T17:04:19.000+0100},
  biburl        = {https://www.bibsonomy.org/bibtex/2d5c2863f93fa8204acd4e4c7065d3e44/trude},
  interhash     = {46651e6a0b410a5dd3bfa84b6c2c3b12},
  intrahash     = {d5c2863f93fa8204acd4e4c7065d3e44},
  issn          = {1613-0073},
  keywords      = {2009 ECML09 _todo recommendation tagging},
  timestamp     = {2010-01-29T17:04:19.000+0100},
  url           = {http://ceur-ws.org/Vol-497/paper_10.pdf},
}

@InProceedings{tian_etal_2013,
  author        = {D. Tian and L. Zhou and Y. Xu and W. Zheng},
  title         = {Microblogger tag auto-annotation based on collective knowledge},
  booktitle     = {2013 IEEE Third International Conference on Information Science and Technology (ICIST)},
  year          = {2013},
  pages         = {221-226},
  month         = {March},
  __markedentry = {[felipe:5]},
  abstract      = {In China, it is free for users to introduce themselves with tags on Sina weibo, the biggest Chinese microblogging platform. User tags provide much useful information for user retrieval and precision advertising. However, 64.2% of users in our study have not tagged themselves. This paper aims to providemicroblogger tag auto-annotation. First, we analyze the tagging behaviors of users to explore what information that is contained in the tagging. Second, based on the analyses and features of social networking, we propose two methods of user tag auto-annotation: one is Collective Filtering (CF) based method and the other is Probabilistic Latent Semantic Analysis (PLSA) based method. Specifically, the CF-based method annotates tags for one user according to the voting scheme by considering his/her neighboring users' tags. By contrast, the PLSA-based method annotates tags for one user according to the topic distribution of his/her neighboring users' tags. Experimental results demonstrate that: (1) the CF-based method outperforms the PLSA-based method at word-level; and (2) the PLSA-based method can obtain more accurate tag annotation results than the CF-based method at topic-level.},
  doi           = {10.1109/ICIST.2013.6747613},
  issn          = {2164-4357},
  keywords      = {Feature extraction;Probabilistic logic;Social network services;Tagging;Vectors;Visualization;Web pages},
}

@Misc{basile_etal_2007,
  author        = {Pierpaolo Basile and Domenico Gendarmi and Giovanni Semeraro},
  title         = {Recommending Smart Tags in a Social Bookmarking System},
  year          = {2007},
  __markedentry = {[felipe:1]},
  url           = {https://pdfs.semanticscholar.org/4dc7/4d79251d5ac69d908e367c22572801f29a18.pdf},
}

@Article{font_etal_2015,
  author          = {Font, F. and Serrà, J. and Serra, X.},
  title           = {Analysis of the impact of a tag recommendation system in a real-world Folksonomy},
  journal         = {ACM Transactions on Intelligent Systems and Technology},
  year            = {2015},
  volume          = {7},
  number          = {1},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis. © 2015 ACM.},
  affiliation     = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Artificial Intelligence Research Institute (IIIA-CSIC), Spanish National Research Council, Bellaterra, Spain},
  art_number      = {6},
  author_keywords = {Experimentation; Measurement; Verification},
  document_type   = {Article},
  doi             = {10.1145/2743026},
  keywords        = {rank3},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2830012.2743026},
}

@InProceedings{si_etal_2009,
  author        = {Si, Xiance and Liu, Zhiyuan and Li, Peng and Jiang, Qixia and Sun, Maosong},
  title         = {Content-based and Graph-based Tag Suggestion},
  booktitle     = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year          = {2009},
  series        = {ECMLPKDDDC'09},
  pages         = {243--260},
  address       = {Aachen, Germany, Germany},
  publisher     = {CEUR-WS.org},
  __markedentry = {[felipe:1]},
  acmid         = {3056167},
  keywords      = {rank2},
  location      = {Bled, Slovenia},
  numpages      = {18},
  url           = {https://www.kde.cs.uni-kassel.de/ws/dc09/results/papers/paper_14.pdf},
}

@Article{kataria_2016,
  author        = {Saurabh Kataria},
  title         = {Recursive Neural Language Architecture for Tag Prediction},
  journal       = {CoRR},
  year          = {2016},
  volume        = {abs/1603.07646},
  __markedentry = {[felipe:1]},
  bibsource     = {dblp computer science bibliography, http://dblp.org},
  biburl        = {http://dblp.uni-trier.de/rec/bib/journals/corr/Kataria16},
  keywords      = {rank3},
  timestamp     = {Wed, 07 Jun 2017 14:43:01 +0200},
  url           = {http://arxiv.org/abs/1603.07646},
}

@InProceedings{wang_etal_2013a,
  author    = {Wang, Hao and Chen, Binyi and Li, Wu-Jun},
  title     = {Collaborative Topic Regression with Social Regularization for Tag Recommendation},
  booktitle = {Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence},
  year      = {2013},
  series    = {IJCAI '13},
  pages     = {2719--2725},
  publisher = {AAAI Press},
  acmid     = {2540520},
  isbn      = {978-1-57735-633-2},
  location  = {Beijing, China},
  numpages  = {7},
  url       = {https://www.cs.ucsb.edu/~binyichen/IJCAI13-400.pdf},
}

@InProceedings{lu_etal_2009,
  author        = {Lu, Yu-Ta and Yu, Shoou-I and Chang, Tsung-Chieh and Hsu, Jane Yung-jen},
  title         = {A Content-based Method to Enhance Tag Recommendation},
  booktitle     = {Proceedings of the 21st International Jont Conference on Artifical Intelligence},
  year          = {2009},
  series        = {IJCAI'09},
  pages         = {2064--2069},
  address       = {San Francisco, CA, USA},
  publisher     = {Morgan Kaufmann Publishers Inc.},
  __markedentry = {[felipe:1]},
  acmid         = {1661775},
  location      = {Pasadena, California, USA},
  numpages      = {6},
  url           = {https://www.aaai.org/ocs/index.php/IJCAI/IJCAI-09/paper/viewFile/621/824},
}

@PhdThesis{choubey_2011,
  author        = {Choubey, Rahul},
  title         = {Tag recommendation using Latent Dirichlet Allocation.},
  school        = {Kansas State University},
  year          = {2011},
  __markedentry = {[felipe:1]},
  added-at      = {2014-07-25T15:53:46.000+0200},
  biburl        = {https://www.bibsonomy.org/bibtex/28dfcc809b6e8ca5cf914b134e1d3592b/schwemmlein},
  interhash     = {6522e8899df29e11c1f8062df9c4f20b},
  intrahash     = {8dfcc809b6e8ca5cf914b134e1d3592b},
  keywords      = {allocation dirichlet latent lda recommendation tag, rank3},
  timestamp     = {2014-07-25T15:53:46.000+0200},
  url           = {http://krex.k-state.edu/dspace/handle/2097/9785},
}

@Comment{jabref-meta: databaseType:bibtex;}

% Encoding: UTF-8

@Article{huang_kuo_2010,
  author   = {H. H. Huang and Y. H. Kuo},
  title    = {Cross-Lingual Document Representation and Semantic Similarity Measure: A Fuzzy Set and Rough Set Based Approach},
  journal  = {IEEE Transactions on Fuzzy Systems},
  year     = {2010},
  volume   = {18},
  number   = {6},
  pages    = {1098-1111},
  month    = {Dec},
  issn     = {1063-6706},
  abstract = {As cross-lingual information retrieval is attracting increasing attention, tools that measure cross-lingual semantic similarity between documents are becoming desirable. In this paper, two aspects of cross-lingual semantic document similarity measures are investigated: One is document representation, and the other is the formulation of similarity measures. Fuzzy set and rough set theories are applied to capture the inherently fuzzy relationships among concepts expressed by natural languages. Our approach first develops a language-independent sense-level document representation based on the fuzzy set model to reduce the barrier between different languages and further explores the fuzzy-rough hybrid approach to obtain a more robust macrosense-level document representation through the partitioning of the integrated sense association network of the document collection into macrosenses. Then, Tversky's notion of similarity and the F1 measure on information retrieval are adopted to formulate, respectively, two document similarity measures with fuzzy set operations on the two proposed document representations. The effectiveness of our approach is demonstrated by its success rate in identifying the English translations to their corresponding Chinese documents in a collection of Chinese-English parallel documents. Moreover, the proposed approach can be easily extended to process documents in other languages. It is believed that the proposed representations, along with the similarity measures, will enable more effective text mining processes.},
  doi      = {10.1109/TFUZZ.2010.2065811},
  keywords = {document handling;fuzzy set theory;information retrieval;natural language processing;rough set theory;Chinese document;Chinese-English parallel document;cross-lingual document representation;cross-lingual information retrieval;cross-lingual semantic document similarity measure;fuzzy set model;fuzzy set operation;fuzzy set theory;integrated sense association network;language-independent sense-level document representation;natural language;rough set theory;text mining process;Approximation methods;Computational modeling;Correlation;Fuzzy sets;Hidden Markov models;Information retrieval;Intelligent systems;Internet;Natural languages;Pragmatics;Robustness;Search engines;Semantics;Set theory;Text mining;Web pages;Cross-lingual;document representation;fuzzy–rough hybrid;sense association network;similarity measure},
}

@InProceedings{elhussein_nakata_2010,
  author    = {M. Elhussein and K. Nakata},
  title     = {Cross-Lingual Information Retrieval as a Side Effect of Tagging},
  booktitle = {2010 IEEE Second International Conference on Social Computing},
  year      = {2010},
  pages     = {582-586},
  month     = {Aug},
  abstract  = {Tagging is used for annotating and categorizing documents, and facilitating information retrieval. Users use tags freely to express their views of the provided content. This freedom allows the user to use any language in tagging. This can be considered as an aid for cross-lingual information retrieval (CLIR) since it can help retrieve documents and other contents from different languages other than the query language. This paper investigates the use of Arabic tags in del.icio.us and analyzes the role they play in CLIR.},
  doi       = {10.1109/SocialCom.2010.92},
  keywords  = {document handling;information retrieval;linguistics;natural language processing;query languages;Arabic tags;cross-lingual information retrieval;document annotation;document categorization;query language;Collaboration;Google;Semantics;Stochastic processes;Tagging;Videos;CLIR;del.icio.us;tagging;user collaboration},
}

@InProceedings{zhao_shang_2010,
  author    = {Z. D. Zhao and M. s. Shang},
  title     = {User-Based Collaborative-Filtering Recommendation Algorithms on Hadoop},
  booktitle = {2010 Third International Conference on Knowledge Discovery and Data Mining},
  year      = {2010},
  pages     = {478-481},
  month     = {Jan},
  abstract  = {Collaborative Filtering (CF) algorithms are widely used in a lot of recommender systems, however, the computational complexity of CF is high thus hinder their use in large scale systems. In this paper, we implement user-based CF algorithm on a cloud computing platform, namely Hadoop, to solve the scalability problem of CF. Experimental results show that a simple method that partition users into groups according to two basic principles, i.e., tidy arrangement of mapper number to overcome the initiation of mapper and partition task equally such that all processors finish task at the same time, can achieve linear speedup.},
  doi       = {10.1109/WKDD.2010.54},
  keywords  = {Internet;computational complexity;information filtering;Hadoop;cloud computing platform;computational complexity;recommender systems;user-based collaborative-filtering recommendation algorithms;Cloud computing;Collaboration;Collaborative work;Computer science;Data engineering;Filtering algorithms;Knowledge engineering;Partitioning algorithms;Recommender systems;Scalability;Map-Reduce;cloud computing;collaborative filtering;hadoop;recommender systems},
}

@Conference{liu_etal_2010,
  author        = {Liu, H. and Zeng, D. and Xia, F. and Li, H.Q.},
  title         = {Bilinear models for item recommendation based on tags},
  booktitle     = {Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics},
  year          = {2010},
  pages         = {463-466},
  month         = {July},
  note          = {cited By},
  abstract      = {Recently, collaborative tagging has been gaining increasing popularity in a large variety of websites. The tags generated from collaborative tagging provide highly abstracted information about users' personal taste on information item, and therefore could be used to profile both users and items. However, flattening the three dimensional user-tag-item matrix into two-way matrices will lead to loss of two dimensional relationships between users, items and tags completely. In this paper, we use a predictive bilinear model to capture the informative interaction patterns among user-tag and item-tag matrices, and employ the Nonnegative Matrix Factorization (NMF) algorithm to extract lower dimensional representative features from tags. Experiments on two real-world datasets show that our approach substantially outperforms the traditional CF methods as well as tag-based recommendation methods reported in the literature. © 2010 IEEE.},
  affiliation   = {Institute of Automation, Chinese Academy of Sciences, China; Department of Management Information Systems, University of Arizona, United States},
  art_number    = {5551605},
  document_type = {Conference Paper},
  doi           = {10.1109/SOLI.2010.5551605},
  journal       = {Proceedings of 2010 IEEE International Conference on Service Operations and Logistics, and Informatics, SOLI 2010},
  keywords      = {Web sites;matrix decomposition;recommender systems;Websites;collaborative tagging;item recommendation;nonnegative matrix factorization algorithm;predictive bilinear model;Measurement},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77957773482&doi=10.1109%2fSOLI.2010.5551605&partnerID=40&md5=62d841aa729dc6847338c8d83a20628e},
}

@InProceedings{chang_wang_2011,
  author    = {H. C. Chang and C. Y. Wang},
  title     = {No Cue, No Clue? Understanding Information Interaction in Social Bookmarking Services},
  booktitle = {2011 Eighth International Conference on Information Technology: New Generations},
  year      = {2011},
  pages     = {867-872},
  month     = {April},
  abstract  = {Social book marking services allow users sharing similar interests to save and exchange web content they find useful or relevant. Book marking is especially important for content not located by the top ranked web pages or easily explored with a traditional search engine. This study investigated the effects that the major context of use (work vs. non-work) has had on experiences with the social book marking service. A within-subject experiment was conducted with 77 subjects. Each took part in both work and non-work information tasks by interacting with six navigation aids. The MANOVA analysis results showed that the contexts of use, work versus non-work, have a significant effect on user information interaction experiences. Moreover, user expertise for work-related tasks has a significant impact on the user perceptions of navigation aids as helpful way finding cues. Understanding how users interact with various way finding cues can enlighten design considerations for refining social navigation functions. For social book marking service providers, the findings of this study offer new insights into user-centered design from an information interaction perspective.},
  doi       = {10.1109/ITNG.2011.150},
  keywords  = {Internet;human computer interaction;search engines;social networking (online);user centred design;MANOVA analysis;Web content exchange;Web pages;information interaction;nonwork information task;search engine;social bookmarking services;social navigation function;user perception;user-centered design;work information task;Collaboration;Context;Human computer interaction;Humans;Information science;Navigation;Tagging;information interaction;social bookmarking;social navigation;social tagging},
}

@InProceedings{ferrer_eerola_2011,
  author    = {R. Ferrer and T. Eerola},
  title     = {Looking Beyond Genres: Identifying Meaningful Semantic Layers from Tags in Online Music Collections},
  booktitle = {2011 10th International Conference on Machine Learning and Applications and Workshops},
  year      = {2011},
  volume    = {2},
  pages     = {112-117},
  month     = {Dec},
  abstract  = {A scheme for identifying the semantic layers of music-related tags is presented. Arguments are provided why the applications of the tags cannot be effectively pursued without a reasonable understanding of their semantic qualities. The identification scheme consists of a set of filters. The first is related with social consensus, user-count ratio, and n-gram properties of tags. The next relies on look-up functions across multiple databases to determine the probable semantic layer of each tag. Examples of the semantic layers with prevalence rates are given based on application of the scheme to a subset of the Million Song Dataset. Finally, a validation of the results was carried out with an independent, smaller hand-annotated dataset, in which high agreement between the identification provided by the scheme and annotations was found.},
  doi       = {10.1109/ICMLA.2011.89},
  keywords  = {information retrieval;music;natural language processing;look-up function;music-related tag;n-gram;online music collection;semantic layer;social consensus;user-count ratio;Databases;Electronic publishing;Encyclopedias;Instruments;Internet;Semantics;music information retrieval;musical genre;semantic layers;social tags},
}

@InProceedings{wu_etal_2012b,
  author    = {Z. Wu and Y. Liu and Y. Wu},
  title     = {Prediction of social tag frequency's power law distribution with RGF model},
  booktitle = {2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year      = {2012},
  pages     = {1884-1889},
  month     = {Oct},
  abstract  = {The power law distribution of social tag frequency is one of the most important statistic characteristics in social tagging system. Researchers have proposed several models to predict this distribution, but these models depend on specific system. What's more, data fitting methods were failed to describe the raw data precisely compared with a mathematical model. Random Group Formation (RGF) model is a newly proposed mathematical model which could predict the power law distribution precisely. This model does not depend on the specific system. It could predict the power law distribution precisely using only three values: total number of elements, groups and the number of elements in the largest group. In this paper, RGF model is employed to predict the power law distribution of tag frequency. The experiments show that the power law distribution of tag frequency can be predicted precisely by three values, and the distribution is independent on specific system. The analysis of the tag dataset using RGF model shows that the exponent of power law decreases with the increasing of the dataset's size.},
  doi       = {10.1109/ICSMC.2012.6378013},
  issn      = {1062-922X},
  keywords  = {data analysis;social networking (online);statistical distributions;RGF model;data fitting method;dataset size;element number value;group value;power law distribution;random group formation model;social tag frequency;social tagging system;statistical characteristics;tag dataset analysis;Market research;Mathematical model;Predictive models;Tagging;RGF model;power law;social tag},
}

@InProceedings{du_etal_2012,
  author    = {W. H. Du and J. W. Rau and J. W. Huang and Y. S. Chen},
  title     = {Improving the quality of tags using state transition on progressive image search and recommendation system},
  booktitle = {2012 IEEE International Conference on Systems, Man, and Cybernetics (SMC)},
  year      = {2012},
  pages     = {3233-3238},
  month     = {Oct},
  abstract  = {In recent years, tagging methods have been widely used on the Web for multimedia search and recommendation systems. Social tags are the keywords annotated by users to the multimedia objects. These tags contain the information which allows multimedia objects to be located and classified. Unfortunately, irrelevant information and noise are frequently included in such tags, especially when we use social tagging systems. Imperfect as they may be, social tags are still a significant source of human-generated contextual knowledge. We present the Waking And Sleeping (WAS) algorithm to overcome the obstacle of noisy tags. WAS refines social tags of each image and assigns each tag into either the waking state or the sleeping state. With WAS, the recommendation system not only enhances the accuracy of the recommendation results based on tags with higher quality but also reduces the consumption of computational resources. The experimental results demonstrate that the proposed approach is superior to compared methods.},
  doi       = {10.1109/ICSMC.2012.6378289},
  issn      = {1062-922X},
  keywords  = {image classification;image retrieval;multimedia computing;social networking (online);WAS algorithm;computational resource consumption;human-generated contextual knowledge;multimedia object classification;multimedia search;noisy tag;progressive image search;recommendation system;sleeping state;social tagging system;state transition;tag quality;tagging method;user annotated keyword;waking and sleeping algorithm;waking state;Accuracy;Databases;Internet;Multimedia communication;Noise measurement;Rabbits;Tagging;Image Annotations;Recommendation System;Tag;Tag Quality Improvement},
}

@InProceedings{mooney_etal_2012,
  author    = {P. Mooney and P. Corcoran and H. Sun and L. Yan},
  title     = {Citizen Generated Spatial Data and Information: Risks and Opportunities},
  booktitle = {2012 International Conference on Industrial Control and Electronics Engineering},
  year      = {2012},
  pages     = {1990-1993},
  month     = {Aug},
  abstract  = {The last few years has seen the use of mobile technology become ubiquituos. Many millions of citizens around the world own smartphones, which they use for both personal and business applications. The majority of smartphones are designed towards Location-based Services (LBS). Consequently these smartphones have on-board GPS devices with the ability of locating the user to an accuracy of a few meters. While at the begining of the growth in popularity of smartphones and LBS technologies most users simply consumed services: where is the nearest coffee shop or subway station? Today, citizens are generating spatial data and information at ever increasing volumes. Examples include: georeferenced photographic collections, location-based social networking such as FourSquare, and volunteered geographic information (VGI). VGI is an exciting movement whereby citizens collect spatial data and information about (their own or another) locality. This content is then shared in collaborative projects such as OpenStreetMap, Geonames, Google Maps Mashups, or WikiMapia. This paper explores the risks involved in using this user-generated spatial data and information with specific emphasis on OpenStreetMap. Most citizens are not specialists in geographic surveying or cartography. Our paper provides results of a large case-study of high edit geographical features. We show that user generated spatial data is a very dynamic but has many inconsistencies.},
  doi       = {10.1109/ICICEE.2012.529},
  keywords  = {Global Positioning System;cartography;geographic information systems;groupware;human computer interaction;mobile computing;social networking (online);user interfaces;FourSquare;Geonames;Google Maps Mashups;LBS;OpenStreetMap;VGI;WikiMapia;cartography;citizen generated spatial data;citizen generated spatial information;collaborative projects;geographic surveying;georeferenced photographic collections;location-based services;location-based social networking;microblogging;mobile technology;onboard GPS devices;smartphones;volunteered geographic information;Communities;Geospatial analysis;Internet;Mobile radio mobility management;Spatial databases;Tagging;Spatial Data;Web 2.0;citizen generated information;data quality},
}

@Article{ivanov_etal_2012,
  author   = {I. Ivanov and P. Vajda and J. S. Lee and T. Ebrahimi},
  title    = {In Tags We Trust: Trust modeling in social tagging of multimedia content},
  journal  = {IEEE Signal Processing Magazine},
  year     = {2012},
  volume   = {29},
  number   = {2},
  pages    = {98-107},
  month    = {March},
  issn     = {1053-5888},
  abstract = {Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.},
  doi      = {10.1109/MSP.2011.942345},
  keywords = {information retrieval;multimedia computing;social networking (online);trusted computing;unsolicited e-mail;multimedia content retrieval;online social networks;social tagging;spam annotations;trust modeling;Content management;Information analysis;Information retrieval;Multimedia communication;Noise measurement;Online services;Search problems;Social network services;Tagging},
}

@Article{sang_etal_2012,
  author   = {J. Sang and C. Xu and J. Liu},
  title    = {User-Aware Image Tag Refinement via Ternary Semantic Analysis},
  journal  = {IEEE Transactions on Multimedia},
  year     = {2012},
  volume   = {14},
  number   = {3},
  pages    = {883-895},
  month    = {June},
  issn     = {1520-9210},
  abstract = {Large-scale user contributed images with tags are easily available on photo sharing websites. However, the noisy or incomplete correspondence between the images and tags prohibits them from being leveraged for precise image retrieval and effective management. To tackle the problem of tag refinement, we propose a method of Ranking based Multi-correlation Tensor Factorization (RMTF), to jointly model the ternary relations among user, image, and tag, and further to precisely reconstruct the user-aware image-tag associations as a result. Since the user interest or background can be explored to eliminate the ambiguity of image tags, the proposed RMTF is believed to be superior to the traditional solutions, which only focus on the binary image-tag relations. During the model estimation, we employ a ranking based optimization scheme to interpret the tagging data, in which the pair-wise qualitative difference between positive and negative examples is used, instead of the point-wise 0/1 confidence. Specifically, the positive examples are directly decided by the observed user-image-tag interrelations, while the negative ones are collected with respect to the most semantically and contextually irrelevant tags. Extensive experiments on a benchmark Flickr dataset demonstrate the effectiveness of the proposed solution for tag refinement. We also show attractive performances on two potential applications as the by-products of the ternary relation analysis.},
  doi      = {10.1109/TMM.2012.2188782},
  keywords = {Web sites;data handling;image retrieval;matrix decomposition;optimisation;semantic Web;tensors;Flickr dataset;RMTF;image retrieval;model estimation;photo sharing Web sites;point-wise 0/1 confidence;ranking based multicorrelation tensor factorization;ranking based optimization scheme;tagging data interpretation;ternary relation analysis;ternary semantic analysis;user-aware image tag refinement;user-aware image-tag associations;user-image-tag interrelations;Correlation;Data models;Media;Noise measurement;Semantics;Tagging;Tensile stress;Factor analysis;social media;tag refinement;tensor factorization},
}

@Article{pammer_etal_2012,
  author          = {Pammer, V. and Kump, B. and Lindstaedt, S.},
  title           = {Tag-based algorithms can predict human ratings of which objects a picture shows},
  journal         = {Multimedia Tools and Applications},
  year            = {2012},
  volume          = {59},
  number          = {2},
  pages           = {441-462},
  note            = {cited By},
  abstract        = {Collaborative tagging platforms allow users to describe resources with freely chosen keywords, so called tags. The meaning of a tag as well as the precise relation between a tag and the tagged resource are left open for interpretation to the user. Although human users mostly have a fair chance at interpreting this relation, machines do not. In this paper we study the characteristics of the problem to identify descriptive tags, i.e. tags that relate to visible objects in a picture. We investigate the feasibility of using a tag-based algorithm, i.e. an algorithm that ignores actual picture content, to tackle the problem. Given the theoretical feasibility of a well-performing tag-based algorithm, which we show via an optimal algorithm, we describe the implementation and evaluation of a WordNet-based algorithm as proof-of-concept. These two investigations lead to the conclusion that even relatively simple and fast tag-based algorithms can yet predict human ratings of which objects a picture shows. Finally, we discuss the inherent difficulty both humans and machines have when deciding whether a tag is descriptive or not. Based on a qualitative analysis, we distinguish between definitional disagreement, difference in knowledge, disambiguation and difference in perception as reasons for disagreement between raters. © Springer Science+Business Media, LLC 2011.},
  affiliation     = {Know-Center, Inffeldgasse 21a, 8010 Graz, Austria; Knowledge Management Institute, Graz University of Technology, Tübingen, Germany},
  author_keywords = {Descriptive tags; Flickr; Image tagging behaviour; Semantic annotation; WordNet},
  document_type   = {Conference Paper},
  doi             = {10.1007/s11042-011-0761-x},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84861886947&doi=10.1007%2fs11042-011-0761-x&partnerID=40&md5=076c3eb2a21d3e836568d288a0de64e0},
}

@Article{zhang_etal_2012,
  author    = {Zi{-}Ke Zhang and Tao Zhou and Yi{-}Cheng Zhang},
  title     = {Tag-Aware Recommender Systems: {A} State-of-the-art Survey},
  journal   = {CoRR},
  year      = {2012},
  volume    = {abs/1202.5820},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1202-5820},
  keywords  = {rank1},
  timestamp = {Wed, 07 Jun 2017 14:43:08 +0200},
  url       = {http://arxiv.org/abs/1202.5820},
}

@Article{lue_etal_2012,
  author   = {Linyuan Lü and Matúš Medo and Chi Ho Yeung and Yi-Cheng Zhang and Zi-Ke Zhang and Tao Zhou},
  title    = {Recommender systems},
  journal  = {Physics Reports},
  year     = {2012},
  volume   = {519},
  number   = {1},
  pages    = {1 - 49},
  issn     = {0370-1573},
  note     = {Recommender Systems},
  doi      = {http://dx.doi.org/10.1016/j.physrep.2012.02.006},
  keywords = {Recommender systems, Information filtering, Networks},
  url      = {http://www.sciencedirect.com/science/article/pii/S0370157312000828},
}

@Article{kolda_bader_2009,
  author     = {Kolda, Tamara G. and Bader, Brett W.},
  title      = {Tensor Decompositions and Applications},
  journal    = {SIAM Rev.},
  year       = {2009},
  volume     = {51},
  number     = {3},
  pages      = {455--500},
  month      = aug,
  issn       = {0036-1445},
  acmid      = {1655230},
  address    = {Philadelphia, PA, USA},
  doi        = {10.1137/07070111X},
  issue_date = {August 2009},
  keywords   = {canonical decomposition (CANDECOMP), higher-order principal components analysis (Tucker), higher-order singular value decomposition (HOSVD), multilinear algebra, multiway arrays, parallel factors (PARAFAC), tensor decompositions},
  numpages   = {46},
  publisher  = {Society for Industrial and Applied Mathematics},
  url        = {http://dx.doi.org/10.1137/07070111X},
}

@Article{treude_storey_2012,
  author   = {C. Treude and M. A. Storey},
  title    = {Work Item Tagging: Communicating Concerns in Collaborative Software Development},
  journal  = {IEEE Transactions on Software Engineering},
  year     = {2012},
  volume   = {38},
  number   = {1},
  pages    = {19-34},
  month    = {Jan},
  issn     = {0098-5589},
  abstract = {In collaborative software development projects, work items are used as a mechanism to coordinate tasks and track shared development work. In this paper, we explore how “tagging,” a lightweight social computing mechanism, is used to communicate matters of concern in the management of development tasks. We present the results from two empirical studies over 36 and 12 months, respectively, on how tagging has been adopted and what role it plays in the development processes of several professional development projects with more than 1,000 developers in total. Our research shows that the tagging mechanism was eagerly adopted by the teams, and that it has become a significant part of many informal processes. Different kinds of tags are used by various stakeholders to categorize and organize work items. The tags are used to support finding of tasks, articulation work, and information exchange. Implicit and explicit mechanisms have evolved to manage the tag vocabulary. Our findings indicate that lightweight informal tool support, prevalent in the social computing domain, may play an important role in improving team-based software development practices.},
  doi      = {10.1109/TSE.2010.91},
  keywords = {groupware;information retrieval;software development management;collaborative software development project;lightweight informal tool support;social computing mechanism;tag vocabulary;team-based software development practice;work item tagging;Collaboration;Data mining;Programming;Software engineering;Tagging;Tagging;articulation work;collaboration;software development;task management;work items., rank2},
}

@Article{kaminskas_ricci_2012,
  author   = {Marius Kaminskas and Francesco Ricci},
  title    = {Contextual music information retrieval and recommendation: State of the art and challenges},
  journal  = {Computer Science Review},
  year     = {2012},
  volume   = {6},
  number   = {2},
  pages    = {89 - 119},
  issn     = {1574-0137},
  doi      = {http://dx.doi.org/10.1016/j.cosrev.2012.04.002},
  keywords = {Music information retrieval, Music recommender systems, Context-aware services, Affective computing, Social computing, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S1574013712000135},
}

@Book{carminati_etal_2013,
  title     = {Security and Trust in Online Social Networks},
  publisher = {Morgan \& Claypool},
  year      = {2013},
  author    = {Barbara Carminati and Elena Ferrari and Marco Viviani},
  isbn      = {9781627052665},
  abstract  = {The enormous success and diffusion that online social networks (OSNs) are encountering nowadays is vastly apparent. Users' social interactions now occur using online social media as communication channels; personal information and activities are easily exchanged both for recreational and business purposes in order to obtain social or economic advantages. In this scenario, OSNs are considered critical applications with respect to the security of users and their resources, for their characteristics alone: the large amount of personal information they manage, big economic upturn connected to their commercial use, strict interconnection among users and resources characterizing them, as well as user attitude to easily share private data and activities with strangers. In this book, we discuss three main research topics connected to security in online social networks: (i) trust management, because trust can be intended as a measure of the perception of security (in terms of risks/benefits) hat users in an OSN have with respect to other (unknown/little-known) parties; (ii) controlled information sharing, because in OSNs, where personal information is not only connected to user profiles, but spans across users' social activities and interactions, users must be provided with the possibility to directly control information flows; and (iii) identity management, because OSNs are subjected more and more to malicious attacks that, with respect to traditional ones, have the advantage of being more effective by leveraging the social network as a new medium for reaching victims. For each of these research topics, in this book we provide both theoretical concepts as well as an overview of the main solutions that commercial/non-commercial actors have proposed over the years. We also discuss some of the most promising research directions in these fields.},
  booktitle = {Security and Trust in Online Social Networks},
  doi       = {10.2200/S00549ED1V01Y201311SPT008},
  pages     = {120-},
  url       = {http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6813068},
}

@InProceedings{zhu_etal_2013,
  author    = {T. Zhu and G. Li and Y. Ren and W. Zhou and P. Xiong},
  title     = {Privacy Preserving for Tagging Recommender Systems},
  booktitle = {2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year      = {2013},
  volume    = {1},
  pages     = {81-88},
  month     = {Nov},
  abstract  = {Tagging recommender systems allow Internet users to annotate resources with personalized tags. The connection among users, resources and these annotations, often called afolksonomy, permits users the freedom to explore tags, and to obtain recommendations. Releasing these tagging datasets accelerates both commercial and research work on recommender systems. However, adversaries may re-identify a user and her/his sensitivity information from the tagging dataset using a little background information. Recently, several private techniques have been proposed to address the problem, but most of them lack a strict privacy notion, and can hardly resist the number of possible attacks. This paper proposes an private releasing algorithm to perturb users' profile in a strict privacy notion, differential privacy, with the goal of preserving a user's identity in a tagging dataset. The algorithm includes three privacy preserving operations: Private Tag Clustering is used to shrink the randomized domain and Private Tag Selection is then applied to find the most suitable replacement tags for the original tags. To hide the numbers of tags, the third operation, Weight Perturbation, finally adds Lap lace noise to the weight of tags We present extensive experimental results on two real world datasets, Delicious and Bibsonomy. While the personalization algorithmis successful in both cases.},
  doi       = {10.1109/WI-IAT.2013.12},
  keywords  = {Internet;data privacy;pattern clustering;random processes;recommender systems;Bibsonomy;De.licio.us;Internet users;Laplace noise;background information;differential privacy;folksonomy;personalization algorithm;personalized tags;privacy-preserving operations;private releasing algorithm;private tag clustering;private tag selection;randomized domain;replacement tags;resource annotation;sensitivity information;tagging datasets;tagging recommender systems;user identity preservation;user profile;weight perturbation;Clustering algorithms;Data privacy;Noise;Privacy;Recommender systems;Sensitivity;Tagging;Differential Privacy;Privacy Preserving;Tagging Recommender System},
}

@InProceedings{6498238,
  author    = {R. M. Feitosa and S. Labidi and A. L. S. dos Santos and N. Santos},
  title     = {Social Recommendation in Location-Based Social Network Using Text Mining},
  booktitle = {2013 4th International Conference on Intelligent Systems, Modelling and Simulation},
  year      = {2013},
  pages     = {67-72},
  month     = {Jan},
  abstract  = {This paper intends to create an application that performs the Data Mining with textual information linked to geolocation data. The structure of the information is distributed in heterogeneous and complex scenario that presents a Social Network. The purpose is that on the final extraction of information, the results are worked for Social Recommendation. However, the recommender systems present some failures in the filtering of the results and the way they are suggested to users. Then, the article presents a methodology of social recommendation to Location-Based Social Network with text mining techniques, and expose issues that still need to research more effective and consolidated results.},
  doi       = {10.1109/ISMS.2013.99},
  issn      = {2166-0662},
  keywords  = {data mining;information filtering;mobile computing;recommender systems;social networking (online);text analysis;complex information distribution;data mining;geolocation data;heterogeneous information distribution;information extraction;information filtering;location-based social network;recommender systems;social recommendation;text mining techniques;textual information;Collaboration;Data mining;Filtration;Frequency measurement;Geology;Recommender systems;Social network services;data mining; social recommendation; tags},
}

@Article{rafailidis_daras_2013,
  author   = {D. Rafailidis and P. Daras},
  title    = {The TFC Model: Tensor Factorization and Tag Clustering for Item Recommendation in Social Tagging Systems},
  journal  = {IEEE Transactions on Systems, Man, and Cybernetics: Systems},
  year     = {2013},
  volume   = {43},
  number   = {3},
  pages    = {673-688},
  month    = {May},
  issn     = {2168-2216},
  abstract = {In this paper, a novel Tensor Factorization and tag Clustering (TFC) model is presented for item recommendation in social tagging systems. The TFC model consists of three distinctive steps, in each of which important innovative elements are proposed. More specifically, through its first step, the content information is exploited to propagate tags between conceptual similar items based on a relevance feedback mechanism, in order to solve sparsity and “cold start” problems. Through its second step, sparsity is further handled, by generating tag clusters and revealing topics, following an innovative tf ·idf weighting scheme. Furthermore, we experimentally prove that a few number of expert tags can improve the performance of quality recommendations, since they contribute to more coherent tag clusters. Through its third step, the latent associations among users, topics, and items are revealed by exploiting the TF technique of high order singular value decomposition. This way the proposed TFC model tackles problems of real-world applications, which produce noise and decrease the quality of recommendations. In our experiments with real-world social data, we show that the proposed TFC model outperforms other state-of-the-art methods, which also exploit the TF technique of HOSVD.},
  doi      = {10.1109/TSMCA.2012.2208186},
  keywords = {pattern clustering;singular value decomposition;social networking (online);tensors;TFC model;content information;innovative elements;item recommendation;quality recommendations;relevance feedback mechanism;singular value decomposition;social tagging systems;tensor factorization and tag clustering;Clustering algorithms;Collaboration;Image color analysis;Recommender systems;Tagging;Tensile stress;Vectors;Content based information retrieval;expert tagging;recommender systems;relevance feedback;social tagging},
}

@InProceedings{lee_etal_2013,
  author    = {I. Lee and G. Cai and K. Lee},
  title     = {Mining Points-of-Interest Association Rules from Geo-tagged Photos},
  booktitle = {2013 46th Hawaii International Conference on System Sciences},
  year      = {2013},
  pages     = {1580-1588},
  month     = {Jan},
  abstract  = {The advent of photo-sharing services results in massive user-generated geo-tagged photos. These photos implicitly and explicitly indicate points-of-interest and their associations. This study aims to combine two data mining techniques: clustering and association rules mining to mine areas of attraction, and their associative patterns. We analyze photos from Flickr in the area of Queensland, Australia, a popular tourist destination hosting the Great Barrier Reef and tropical rain forest. We report interesting experimental results and discuss findings.},
  doi       = {10.1109/HICSS.2013.401},
  issn      = {1530-1605},
  keywords  = {data mining;pattern clustering;social networking (online);Australia;Flickr;Great Barrier Reef;Queensland;associative patterns;attraction area mining;clustering;data mining techniques;photo-sharing services;points-of-interest association rules mining;tropical rain forest;user-generated geo-tagged photos;Association rules;Australia;Business;Educational institutions;Itemsets;Noise;Flickr mining;Point of interest mining;geo-tagged photos},
}

@InProceedings{tang_etal_2013b,
  author    = {X. Tang and Y. Xu and S. Geva},
  title     = {Integrating Time Forgetting Mechanisms into Topic-Based User Interest Profiling},
  booktitle = {2013 IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI) and Intelligent Agent Technologies (IAT)},
  year      = {2013},
  volume    = {3},
  pages     = {1-4},
  month     = {Nov},
  abstract  = {The rapid development of the World Wide Web has created massive information leading to the information overload problem. Under this circumstance, personalization techniques have been brought out to help users in finding content which meet their personalized interests or needs out of massively increasing information. User profiling techniques have performed the core role in this research. Traditionally, most user profiling techniques create user representations in a static way. However, changes of user interests may occur with time in real world applications. In this research we develop algorithms for mining user interests by integrating time decay mechanisms into topic-based user interest profiling. Time forgetting functions will be integrated into the calculation of topic interest measurements on in-depth level. The experimental study shows that, considering temporal effects of user interests by integrating time forgetting mechanisms shows better performance of recommendation.},
  doi       = {10.1109/WI-IAT.2013.132},
  keywords  = {data mining;recommender systems;user interfaces;World Wide Web;information overload problem;personalization techniques;temporal effects;time decay mechanisms;time forgetting mechanisms;topic interest measurements;topic-based user interest profiling;user interests mining;user needs;user personalized interests;user profiling techniques;user representations;Accuracy;Collaboration;Equations;Probability;Recommender systems;Tagging;recommender systems;temporal dynamics;time forgetting;user profiling},
}

@InProceedings{misaghian_etal_2013,
  author    = {N. Misaghian and M. Jalali and M. H. Moattar},
  title     = {Resource recommender system based on tag and time for social tagging system},
  booktitle = {ICCKE 2013},
  year      = {2013},
  pages     = {97-101},
  month     = {Oct},
  abstract  = {Recently social tagging systems are increasingly becoming popular. These systems allow users to manage, organize and search their required resources. Most of the available modules in recommender systems, don't consider information such as time that could be effective on the users preferences of a resource. So, one of the existing challenges in resource recommendation systems is the appropriate integration of tag and time information to discover user's taste considering time drift and improving accuracy. In this paper data from social tagging systems are modeled with four-mode tensors to capture four-way correlations between users, items, tags and time information. Then by applying multi-way analysis, latent correlations among users preferences based on time information are revealed, which help to improve the quality of recommendations. The advantages of proposed system are that it considers simultaneous relations among elements, discovers latent associations and improves the quality of recommendations accuracy. The experimental results on a real-world dataset, Citeulike, indicate significant improvements in recommendation quality and better performance compared with the existing systems.},
  doi       = {10.1109/ICCKE.2013.6682832},
  keywords  = {recommender systems;social networking (online);tensors;Citeulike dataset;four-mode tensors;multiway analysis;resource recommender system;social tagging system;time drift;time information;user preferences;Accuracy;Chaos;Context;Lead;Tagging;Tensile stress;Social tagging system;collaborative recommendation system;tensor},
}

@InProceedings{moghaddam_etal_2013,
  author    = {M. G. Moghaddam and A. Mustapha and N. Mustapha and N. M. Sharef},
  title     = {A temporal-focused trustworthiness to enhance trust-based recommender systems},
  booktitle = {2013 13th International Conference on Intellient Systems Design and Applications},
  year      = {2013},
  pages     = {219-223},
  month     = {Dec},
  abstract  = {Collaborative Filtering (CF) is the most successful technology for recommender systems. The technology does not rely on actual content of the items, but instead requires users to indicate preferences, most commonly in the form of ratings. While CF is known for its traditional problems such as cold-start, sparsity and modest accuracy, a trust-based CF has been previously proposed to solve such issues by focusing on trust values among the users. Nonetheless, all existing trust-based approaches use trust as a factor independent from scope, whether explicit or implicit. We argue that trustworthiness should not be the same across all conditions; hence the trust values should change to suit certain scope or focused area. To validate the proposed temporal-focused trustworthiness in this paper, we propose a novel pheromone-based approach to calculate trustworthiness by focusing on time factor. Implementation of the proposed approach is hoped to reduce cold-start and sparsity as well as improve accuracy of the recommendation results.},
  doi       = {10.1109/ISDA.2013.6920738},
  issn      = {2164-7143},
  keywords  = {collaborative filtering;recommender systems;trusted computing;cold-start;collaborative filtering;pheromone-based approach;temporal-focused trustworthiness;time factor;trust values;trust-based CF;trust-based recommender systems;Expert systems;Recommender systems;collaborative filtering;social tagging systems;temporal-focused;trust},
}

@Article{bobadilla_etal_2013,
  author   = {J. Bobadilla and F. Ortega and A. Hernando and A. Gutiérrez},
  title    = {Recommender systems survey},
  journal  = {Knowledge-Based Systems},
  year     = {2013},
  volume   = {46},
  pages    = {109 - 132},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2013.03.012},
  keywords = {Recommender systems, Collaborative filtering, Similarity measures, Evaluation metrics, Prediction, Recommendation, Hybrid, Social, Internet of things, Cold-start},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705113001044},
}

@Article{liu_etal_2013b,
  author   = {Jing Liu and Yifan Zhang and Zechao Li and Hanqing Lu},
  title    = {Correlation consistency constrained probabilistic matrix factorization for social tag refinement},
  journal  = {Neurocomputing},
  year     = {2013},
  volume   = {119},
  pages    = {3 - 9},
  issn     = {0925-2312},
  note     = {Intelligent Processing Techniques for Semantic-based Image and Video Retrieval},
  doi      = {http://dx.doi.org/10.1016/j.neucom.2012.02.052},
  keywords = {Matrix factorization, Correlation consistency, Tag refinement, Social image},
  url      = {http://www.sciencedirect.com/science/article/pii/S0925231213000192},
}

@Article{jung_2013,
  author   = {Jason J. Jung},
  title    = {Cross-lingual query expansion in multilingual folksonomies: A case study on Flickr},
  journal  = {Knowledge-Based Systems},
  year     = {2013},
  volume   = {42},
  pages    = {60 - 67},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2013.01.016},
  keywords = {Social searching, Collective intelligence, Multilingual tagging, Multilingual folksonomy, Lingual practice},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705113000282},
}

@Article{gan_jiang_2013,
  author   = {Mingxin Gan and Rui Jiang},
  title    = {Improving accuracy and diversity of personalized recommendation through power law adjustments of user similarities},
  journal  = {Decision Support Systems},
  year     = {2013},
  volume   = {55},
  number   = {3},
  pages    = {811 - 821},
  issn     = {0167-9236},
  doi      = {http://dx.doi.org/10.1016/j.dss.2013.03.006},
  keywords = {Recommender systems, Collaborative filtering, Power law adjustment, Accuracy, Diversity},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167923613000882},
}

@Article{spiranec_ivanjko_2013,
  author   = {Sonja Špiranec and Tomislav Ivanjko},
  title    = {Experts vs. Novices Tagging Behavior: An Exploratory Analysis},
  journal  = {Procedia - Social and Behavioral Sciences},
  year     = {2013},
  volume   = {73},
  pages    = {456 - 459},
  issn     = {1877-0428},
  note     = {Proceedings of the 2nd International Conference on Integrated Information (IC-ININFO 2012), Budapest, Hungary, August 30 – September 3, 2012},
  doi      = {http://dx.doi.org/10.1016/j.sbspro.2013.02.076},
  keywords = {folksonomies, tagging, tag efficiency, tagging behavior},
  url      = {http://www.sciencedirect.com/science/article/pii/S1877042813003698},
}

@InProceedings{rognon_etal_2013,
  author    = {T. Rognon and R. Strautman and L. Jett and N. Bigdely-Shamlo and S. Makeig and T. Johnson and K. Robbins},
  title     = {CTAGGER: Semi-structured community tagging for annotation and data-mining in event-rich contexts},
  booktitle = {2013 IEEE Global Conference on Signal and Information Processing},
  year      = {2013},
  pages     = {5-8},
  month     = {Dec},
  abstract  = {Analysis of dynamic brain imaging data from EEG, MEG or fMRI requires a common temporal context to enable meta-analysis and data mining across experiments. However, there is no standardized method of annotating events, even from laboratory experiments in controlled settings, and the event-rich environments of real-world brain imaging present a still greater annotation challenge. We have developed a MATLAB toolbox called CTAGGER to enable a user-friendly, semi-structured and expandable strategy for event annotation in dynamic brain imaging and other time-series. To facilitate common labeling and comparison across data collections and laboratories, an individual's annotation can be collected in a common community database to encourage annotation reuse. The tools allow the creation of multiple event overlays to facilitate the reuse and combination of brain imaging data for multiple analyses.},
  doi       = {10.1109/GlobalSIP.2013.6736797},
  keywords  = {biomedical MRI;brain;data acquisition;data analysis;data mining;electroencephalography;human computer interaction;information retrieval;magnetoencephalography;medical computing;meta data;social networking (online);time series;CTAGGER;EEG;MEG;Matlab toolbox;data annotation;data collection;data mining;dynamic brain imaging data analysis;event annotation reusability;expandable strategy;fMRI;meta analysis;semi-structured community tagging;time series;user friendly strategy;Electroencephalography;Laboratories;MATLAB;Roads;Shape;Vehicles;Visualization},
}

@Article{alves_santanche_2013a,
  author   = {Hugo Alves and André Santanchè},
  title    = {Folksonomized ontology and the 3E steps technique to support ontology evolvement},
  journal  = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year     = {2013},
  volume   = {18},
  number   = {1},
  pages    = {19 - 30},
  issn     = {1570-8268},
  note     = {Special Section on the Semantic and Social Web},
  doi      = {http://dx.doi.org/10.1016/j.websem.2012.07.004},
  keywords = {Ontology, Folksonomy, Semantic web, Folksonomized ontology},
  url      = {http://www.sciencedirect.com/science/article/pii/S1570826812000789},
}

@Conference{kailer_etal_2013,
  author          = {Kailer, D. and Mandl, P. and Schill, A.},
  title           = {Rated tags: Adding rating capability to collaborative tagging},
  booktitle       = {2013 International Conference on Cloud and Green Computing},
  year            = {2013},
  pages           = {249-255},
  month           = {Sept},
  note            = {cited By},
  abstract        = {Collaborative tagging is a popular way to organize content. But sometimes it is also used as a way to express opinions, which is normally done through rating-or text review systems. This paper will demonstrate that there is a gap between rating- and text review systems, that limits the ability of users to find the desired item. A novel concept based on collaborative tagging is presented, which is designed to bridge this gap. This concept, named Rated Tags, uses a hybrid approach to combine tagging-, rating- and review functionality. A key element of Rated Tags is the combination of a traditional user generated tag and a 5-star rating scale. Such a tag is called rated tag. We will discuss the design and the challenges of Rated Tags in detail and demonstrate its application based on a prototype. Furthermore we will show that similar related works suffer from an ambiguity problem, which was avoided in Rated Tags. © 2013 IEEE.},
  affiliation     = {Munich University of Applied Sciences, Department of Computer Science and Mathematics, Munich, Germany; TU Dresden, Department of Computer Networks, Dresden, Germany},
  art_number      = {6686038},
  author_keywords = {Collaborative tagging; Rated tags; Ratings; Reviews; Social tagging; Tags},
  document_type   = {Conference Paper},
  doi             = {10.1109/CGC.2013.46},
  journal         = {Proceedings - 2013 IEEE 3rd International Conference on Cloud and Green Computing, CGC 2013 and 2013 IEEE 3rd International Conference on Social Computing and Its Applications, SCA 2013},
  keywords        = {collaborative filtering;information retrieval;text analysis;5-star rating scale;collaborative filtering;collaborative tagging;rated tags;rating functionality;rating review systems;review functionality;text review systems;Bridges;Cameras;Collaboration;Decision making;Headphones;Prototypes;Tagging;collaborative tagging;rated tags;ratings;reviews;social tagging;tags},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84893300880&doi=10.1109%2fCGC.2013.46&partnerID=40&md5=b6d46ec4461cf9033b7e640f6770bf2d},
}

@Article{uddin_etal_2013,
  author          = {Uddin, M.N. and Duong, T.H. and Nguyen, N.T. and Qi, X.-M. and Jo, G.S.},
  title           = {Semantic similarity measures for enhancing information retrieval in folksonomies},
  journal         = {Expert Systems with Applications},
  year            = {2013},
  volume          = {40},
  number          = {5},
  pages           = {1645-1653},
  issn            = {0957-4174},
  note            = {cited By},
  abstract        = {Collaborative tagging systems, also known as folksonomies, enable a user to annotate various web resources with a free set of tags for sharing and searching purposes. Tags in a folksonomy reflect users' collaborative cognition about information. Tags play an important role in a folksonomy as a means of indexing information to facilitate search and navigation of resources. However, the semantics of the tags, and therefore the semantics of the resources, are neither known nor explicitly stated. It is therefore difficult for users to find related resources due to the absence of a consistent semantic meaning among tags. The shortage of relevant tags increases data sparseness and decreases the rate of information extraction with respect to user queries. Defining semantic relationships between tags, resources, and users is an important research issue for the retrieval of related information from folksonomies. In this research, a method for finding semantic relationships among tags is proposed. The present study considers not only the pairwise relationships between tags, resources, and users, but also the relationships among all three. Experimental results using real datasets from Flickr and Del.icio.us show that the method proposed here is more effective than previous methods such as LCH, JCN, and LIN in finding semantic relationships among tags in a folksonomy. © 2012 Elsevier Ltd. All rights reserved.},
  affiliation     = {School of Computer and Information Engineering, Inha University, South Korea; Institute of Informatics, Wroclaw University of Technology, Poland},
  author_keywords = {Collaborative tagging; Folksonomies; Information retrieval; Tag; WordNet},
  document_type   = {Article},
  doi             = {10.1016/j.eswa.2012.09.006},
  keywords        = {Folksonomies, Tag, WordNet, Information retrieval, Collaborative tagging},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84872026953&doi=10.1016%2fj.eswa.2012.09.006&partnerID=40&md5=072779fe68162ffeca6711eb851cb292},
}

@InProceedings{amarai_etal_2014,
  author    = {A. Roque do Amarai and L. A. M. Zaina and J. F. Rodrigues and R. de Aguiar and M. C. Barboza},
  title     = {The use of social tagging to support the cataloguing of learning objects},
  booktitle = {2014 IEEE Frontiers in Education Conference (FIE) Proceedings},
  year      = {2014},
  pages     = {1-7},
  month     = {Oct},
  abstract  = {Social networks have become the main media for information dissemination in the so-called Web 2.0. The core of these networks is social tagging, the act of annotating what users see in their social space. In the education domain, social tagging is potentially a useful resource to improve the organization (cataloguing) of large repositories of learning objects. To the present moment, however, many questions are open about social tagging in e-learning. In this work, hence, we proceed to answer three questions: (1) Can social tagging successfully catalog e-learning objects? (2) How do students behave according to Körner's classification: categorizers or describers? and (3) Does social tagging converge to a well-defined descriptive vocabulary of tags? We performed a large experiment with 336 technician students that marked 218 electronic learning objects for about 4,985 times. Our results show that social tagging is a promising practice for e-learning; however some issues must be addressed to prevent an excessive number of categorizer students and, also, a premature convergence of the vocabulary of tags. Our conclusions are specific for the setting of our experiment, but we generalize them as much as possible suggesting guidelines of how to use social tagging in e-learning.},
  doi       = {10.1109/FIE.2014.7044487},
  issn      = {0190-5848},
  keywords  = {Internet;cataloguing;computer aided instruction;information dissemination;pattern classification;social networking (online);Körner classification;Web 2.0;descriptive vocabulary;e-learning;education domain;information dissemination;learning object;organization cataloguing;social network;social tagging;Catalogs;Computer science;Educational institutions;Electronic learning;Search problems;Tagging;Vocabulary;folksonomy;learning objects;social tagging},
}

@InProceedings{manca_etal_2014,
  author    = {M. Manca and L. Boratto and S. Carta},
  title     = {Design and architecture of a friend recommender system in the social bookmarking domain},
  booktitle = {2014 Science and Information Conference},
  year      = {2014},
  pages     = {838-842},
  month     = {Aug},
  abstract  = {Social media systems are becoming more and more popular nowadays. In order to face the overload in the amount of users and content available in these systems, social recommender systems have been developed and are largely studied in the literature. A form of social media, known as social bookmarking system, allows to share bookmarks in a social network. A user adds as a friend or follows another user and receives updates on the bookmarks added by that user. However, no approach in the literature proposes friend recommender systems in the social bookmarking domain. In this paper, we present an analysis of the state-of-the-art on user recommendation in social environments and of the structure of a social bookmarking system, in order to derive a design and an architecture of a friend recommender system in the social bookmarking domain. This study can be useful for any future research in this area, by highlighting the aspects that characterize this domain and the features that this type of recommender system has to offer.},
  doi       = {10.1109/SAI.2014.6918282},
  keywords  = {recommender systems;social networking (online);friend recommender system architecture;friend recommender system design;social bookmarking domain;social environments;social media systems;social network;social recommender systems;user recommendation;Computer architecture;Design methodology;Media;Recommender systems;Tagging;Twitter;Friend Recommendation;Social Bookmarking;Tagging System},
}

@Article{lee_etal_2014,
  author   = {Baozhen Lee and Weiguo Fan and Anna C. Squicciarini and Shilun Ge and Yun Huang},
  title    = {The relativity of privacy preservation based on social tagging},
  journal  = {Information Sciences},
  year     = {2014},
  volume   = {288},
  pages    = {87 - 107},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2014.08.015},
  keywords = {Privacy preservation, Social tagging, Relativity, Hierarchical community},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025514008044},
}

@Article{wagner_etal_2014,
  author   = {C. Wagner and P. Singer and M. Strohmaier and B. Huberman},
  title    = {Semantic Stability and Implicit Consensus in Social Tagging Streams},
  journal  = {IEEE Transactions on Computational Social Systems},
  year     = {2014},
  volume   = {1},
  number   = {1},
  pages    = {108-120},
  month    = {March},
  issn     = {2329-924X},
  abstract = {One potential disadvantage of social tagging systems is that due to the lack of a centralized vocabulary, a crowd of users may never manage to reach a consensus on the description of resources (e.g., books, images, users, or songs) on the Web. Yet, previous research has provided interesting evidence that the tag distributions of resources in social tagging systems may become semantically stable over time, as more and more users tag them and implicitly agree on the relative importance of tags for a resource. At the same time, previous work has raised an array of new questions such as: 1) how can we assess semantic stability in a robust and methodical way? 2) does the semantic stabilization varies across different social tagging systems and ultimately, and 3) what are the factors that can explain semantic stabilization in such systems? In this work, we tackle these questions by: 1) presenting a novel and robust method, which overcomes a number of limitations in existing methods; 2) empirically investigating semantic stabilization in different social tagging systems with distinct domains and properties; and 3) detecting potential causes of stabilization and implicit consensus, specifically imitation behavior, shared background knowledge and intrinsic properties of natural language. Our results show that tagging streams that are generated by a combination of imitation dynamics and shared background knowledge exhibit faster and higher semantic stability than tagging streams that are generated via imitation dynamics or natural language phenomena alone.},
  doi      = {10.1109/TCSS.2014.2307455},
  keywords = {information retrieval;natural language processing;centralized vocabulary;imitation dynamics;implicit consensus;natural language phenomena;semantic stability;semantic stabilization;social tagging systems;Natural languages;Robustness;Semantics;Stability analysis;Tagging;Twitter;Vocabulary;Distributional semantics;emergent semantics;social semantics;social tagging;stabilization process},
}

@InProceedings{tian_etal_2014,
  author    = {Y. Tian and D. Lo and J. Lawall},
  title     = {Automated construction of a software-specific word similarity database},
  booktitle = {2014 Software Evolution Week - IEEE Conference on Software Maintenance, Reengineering, and Reverse Engineering (CSMR-WCRE)},
  year      = {2014},
  pages     = {44-53},
  month     = {Feb},
  abstract  = {Many automated software engineering approaches, including code search, bug report categorization, and duplicate bug report detection, measure similarities between two documents by analyzing natural language contents. Often different words are used to express the same meaning and thus measuring similarities using exact matching of words is insufficient. To solve this problem, past studies have shown the need to measure the similarities between pairs of words. To meet this need, the natural language processing community has built WordNet which is a manually constructed lexical database that records semantic relations among words and can be used to measure how similar two words are. However, WordNet is a general purpose resource, and often does not contain software-specific words. Also, the meanings of words in WordNet are often different than when they are used in software engineering context. Thus, there is a need for a software-specific WordNet-like resource that can measure similarities of words. In this work, we propose an automated approach that builds a software-specific WordNet like resource, named WordSimSEDB, by leveraging the textual contents of posts in StackOverflow. Our approach measures the similarity of words by computing the similarities of the weighted co-occurrences of these words with three types of words in the textual corpus. We have evaluated our approach on a set of software-specific words and compared our approach with an existing WordNet-based technique (WordNetres) to return top-k most similar words. Human judges are used to evaluate the effectiveness of the two techniques. We find that WordNetres returns no result for 55 % of the queries. For the remaining queries, WordNetres returns significantly poorer results.},
  doi       = {10.1109/CSMR-WCRE.2014.6747213},
  keywords  = {database management systems;natural language processing;program debugging;software engineering;StackOverflow;WordNet;WordSimSEDB;automated construction;automated software engineering;bug report categorization;code search;duplicate bug report detection;lexical database;natural language contents;natural language processing;software-specific word similarity database;Java;Measurement;Semantics;Software;Software engineering;Tuning;Vectors, rank1},
}

@Article{du_etal_2014,
  author   = {Helen S. Du and Samuel K.W. Chu and Gary E. Gorman and Felix L.C. Siu},
  title    = {Academic social bookmarking: An empirical analysis of Connotea users},
  journal  = {Library \& Information Science Research},
  year     = {2014},
  volume   = {36},
  number   = {1},
  pages    = {49 - 58},
  issn     = {0740-8188},
  doi      = {http://dx.doi.org/10.1016/j.lisr.2013.06.003},
  keywords = {rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0740818814000036},
}

@Article{chen_etal_2014b,
  author          = {Chen, Z. and Gao, Q. and Yang, Y.},
  title           = {Cultural difference on the usage pattern of tagging system for knowledge sharing},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2014},
  volume          = {8528 LNCS},
  pages           = {534-545},
  note            = {cited By},
  abstract        = {Collaborative tagging system in online knowledge sharing system benefits the knowledge management of users. It also inspires us a quantitative way how to investigate the effect of culture on knowledge management as tags reflects the usage pattern of users. In this study, the usage pattern of tag in three types of knowledge management websites (i.e. photo, bookmark and book sharing websites) present the influence of Chinese culture upon knowledge management. A set of indices is used to describe the usage pattern of tags and the results indicate that Chinese users behave differently in all three types of knowledge sharing system. © 2014 Springer International Publishing.},
  affiliation     = {Institute of Human Factors and Ergonomics, Industrial Engineering, Tsinghua University, China},
  author_keywords = {Chinese culture; knowledge sharing; tag},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-319-07308-8_51},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84903603782&doi=10.1007%2f978-3-319-07308-8_51&partnerID=40&md5=40600e69ab0b3a7ebb4d3e982ceb3b2c},
}

@Conference{lorince_etal_2014,
  author          = {Lorince, J. and Zorowitz, S. and Murdock, J. and Todd, P.M.},
  title           = {Supertagger behavior in building folksonomies},
  year            = {2014},
  pages           = {129-138},
  note            = {cited By},
  abstract        = {A folksonomy is ostensibly an information structure built up by the "wisdom of the crowds", but is the "crowd" really doing the work? Tagging is in fact a sharply skewed process in which a small minority of users generate an overwhelming majority of the annotations. Using data from the social music site Last.fm as a case study, this paper explores the implications of this tagging imbalance. Partitioning the folksonomy into two halves - one created by the prolific minority and the other by the non-prolific majority of taggers - we examine the large-scale differences in these two sub-folksonomies and the users generating them, and then explore several possible accounts of what might be driving these differences. We find that prolific taggers preferentially annotate content in the long-tail of less popular items, use tags with higher information content, and show greater tagging expertise. These results indicate that "supertaggers" not only tag more than their counterparts, but in quantifi- ably different ways. Copyright © 2014 ACM.},
  affiliation     = {Cognitive Science, Indiana University, United States; Psychological and Brain Sciences, Indiana University, United States; Informatics John Hopkins University, United States},
  author_keywords = {Collaborative tagging; Folksonomy; Supertaggers},
  document_type   = {Conference Paper},
  doi             = {10.1145/2615569.2615686},
  journal         = {WebSci 2014 - Proceedings of the 2014 ACM Web Science Conference},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84904489299&doi=10.1145%2f2615569.2615686&partnerID=40&md5=939db123f609a67ed4137dd8d3ca3fd1},
}

@Article{santos-neto_etal_2014,
  author        = {Santos-Neto, E. and Condon, D. and Andrade, N. and Iamnitchi, A. and Ripeanu, M.},
  title         = {Reuse, temporal dynamics, interest sharing, and collaboration in social tagging system},
  journal       = {First Monday},
  year          = {2014},
  volume        = {19},
  number        = {7},
  note          = {cited By},
  abstract      = {User-generated content shapes the dynamics of the World Wide Web. In particular, collaborative tagging represents a simple, yet powerful, feature that enables users to share and collaboratively annotate content such as photos and URLs. This collaborative behavior and the pool of user-generated metadata create opportunities to improve existing systems and to design new mechanisms. However, to realize this potential, it is necessary to first understand the usage characteristics of current systems. This work addresses this issue by characterizing three tagging systems (CiteULike, Connotea and del.icio.us) while focusing on three aspects: i) the patterns of information (tags and items) production; ii) the temporal dynamics of users' tag vocabularies; and, iii) the social aspects of tagging systems. The analysis of the patterns of information production shows that users publish new content more often than they annotate already existing content in the system. The opposite, however, occurs for tags; the level of tag reuse is much higher. The study of the temporal dynamics of user vocabularies shows that the growth rate of tag vocabularies across the user population over time decreases at early ages, stabilizes, and returns to increase for older users. Moreover, a closer look into the change of vocabulary contents over time shows that despite the fact that tag vocabularies are slowly growing in size with user age, the relative frequency in which each tag is used converges relatively quickly in a users lifetime. Finally, the characterization of social aspects of tagging unveils the relationship between the implicit user ties, as inferred from the similarity between users' activity, and their explicit social ties, as represented by co-membership in discussion groups or semantic similarity between tag vocabularies. © First Monday, 1995-2014.},
  affiliation   = {Electrical and Computer Engineering Department, University of British Columbia, Canada; University of South Florida, United States; Systems and Computing Department, Universidade Federal de Campina Grande, Brazil},
  document_type = {Article},
  doi           = {10.5210/fm.v19i7.4994},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84906057489&doi=10.5210%2ffm.v19i7.4994&partnerID=40&md5=0dfd833d70fe6eb7dfcf8b7a482ede17},
}

@Article{parra-arnau_etal_2014,
  author          = {J. Parra-Arnau and A. Perego and E. Ferrari and J. Forné and D. Rebollo-Monedero},
  title           = {Privacy-Preserving Enhanced Collaborative Tagging},
  journal         = {IEEE Transactions on Knowledge and Data Engineering},
  year            = {2014},
  volume          = {26},
  number          = {1},
  pages           = {180-193},
  month           = {Jan},
  issn            = {1041-4347},
  note            = {cited By},
  abstract        = {Collaborative tagging is one of the most popular services available online, and it allows end user to loosely classify either online or offline resources based on their feedback, expressed in the form of free-text labels (i.e., tags). Although tags may not be per se sensitive information, the wide use of collaborative tagging services increases the risk of cross referencing, thereby seriously compromising user privacy. In this paper, we make a first contribution toward the development of a privacy-preserving collaborative tagging service, by showing how a specific privacy-enhancing technology, namely tag suppression, can be used to protect end-user privacy. Moreover, we analyze how our approach can affect the effectiveness of a policy-based collaborative tagging system that supports enhanced web access functionalities, like content filtering and discovery, based on preferences specified by end users.},
  affiliation     = {Department of Telematics Engineering, Universitat Politècnica de Catalunya, Campus Nord, C./ Jordi Girona 1-3, 08034 Barcelona, Spain; European Commission - Joint Research Centre of the European Commission, Institute for Environment and Sustainability, Unit H06 - Digital Earth and Reference Data, Via E. Fermi, 2749, TP 262, 21027 Ispra VA, Italy; Department of Theoretical and Applied Science, University of Insubria, Via Mazzini 5, I-21100 Varese, Italy},
  art_number      = {6399467},
  author_keywords = {Policy-based collaborative tagging; Privacy-enhancing technology; Privacy-utility tradeoff; Shannon's entropy; Social bookmarking; Tag suppression},
  document_type   = {Article},
  doi             = {10.1109/TKDE.2012.248},
  keywords        = {data privacy;social networking (online);Web access functionalities;collaborative tagging services;content discovery;content filtering;cross referencing;end-user privacy protection;free-text labels;offline resources;online resources;policy-based collaborative tagging system;privacy-enhancing technology;privacy-preserving collaborative tagging service;social bookmarking;tag suppression;Collaboration;Data privacy;Entropy;Privacy;Semantics;Tag clouds;Policy-based collaborative tagging;Shannon's entropy;privacy-enhancing technology;privacy-utility tradeoff;social bookmarking;tag suppression},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84890419962&doi=10.1109%2fTKDE.2012.248&partnerID=40&md5=10d4503770c0b996ed99128e65b0e4ff},
}

@Article{carvalho_etal_2015,
  author   = {Carlos Rosemberg Maia de Carvalho and Elizabeth Sucupira Furtado and Vasco Furtado},
  title    = {Does content categorization lead to knowledge building? An experiment in a social bookmarking service},
  journal  = {Computers in Human Behavior},
  year     = {2015},
  volume   = {51},
  pages    = {1177 - 1184},
  issn     = {0747-5632},
  note     = {Computing for Human Learning, Behaviour and Collaboration in the Social and Mobile Networks Era},
  doi      = {http://dx.doi.org/10.1016/j.chb.2015.01.033},
  keywords = {User studies, Social tagging, Collaborative filtering, Collaborative learning, Empirical studies in HCI, Social reputation systems},
  url      = {http://www.sciencedirect.com/science/article/pii/S0747563215000473},
}

@Article{lorince_etal_2015,
  author          = {Lorince, J. and Joseph, K. and Todd, P.M.},
  title           = {Analysis of music tagging and listening patterns: Do tags really function as retrieval aids?},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2015},
  volume          = {9021},
  pages           = {141-152},
  note            = {cited By},
  abstract        = {In collaborative tagging systems, it is generally assumed that users assign tags to facilitate retrieval of content at a later time. There is, however, little behavioral evidence that tags actually serve this purpose. Using a large-scale dataset from the social music website Last.fm, we explore how patterns of music tagging and subsequent listening interact to determine if there exist measurable signals of tags functioning as retrieval aids. Specifically, we describe our methods for testing if the assignment of a tag tends to lead to an increase in listening behavior. Results suggest that tagging, on average, leads to only very small increases in listening rates, and overall the data do not support the assumption that tags generally serve as retrieval aids. © Springer International Publishing Switzerland 2015.},
  affiliation     = {Cognitive Science Program, Indiana University, Bloomington, IN, United States; Computation, Organization, and Society Program, Carnegie Mellon University, Pittsburgh, PA, United States},
  author_keywords = {Collaborative tagging; Folksonomy; Memory cues; Music listening; Personal information management; Retrieval aids},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-319-16268-3_15},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84925321873&doi=10.1007%2f978-3-319-16268-3_15&partnerID=40&md5=f8564aec2ec258ec3851af8068213779},
}

@Conference{klyachko_2015,
  author          = {Klyachko, E.},
  title           = {Using folksonomy data for determining semantic similarity},
  year            = {2015},
  volume          = {2},
  number          = {14},
  pages           = {119-132},
  note            = {cited By},
  abstract        = {This paper presents a method for measuring semantic similarity. Semantic similarity measures are important for various semantics-oriented natural language processing tasks, such as Textual Entailment or Word Sense Disambiguation. In the paper, a folksonomy graph is used to determine the relatedness of two words. The construction of a folksonomy from a collaborative photo tagging resource is described. The problems which occur during the process are analyzed and solutions are proposed. The structure of the folksonomy is also analyzed. It turns out to be a social network graph. Graph features, such as the path length, or the Jaccard similarity coefficient, are the input parameters for a machine learning classifying algorithm. The comparative importance of the parameters is evaluated. Finally, the method was evaluated in the RUSSE evaluation campaign. The results are lower than most results for distribution-based vector models. However, the model itself is cheaper to build. The failures of the models are analyzed and possible improvements are suggested.},
  affiliation     = {Moscow, Russian Federation},
  author_keywords = {Collaborative tagging; Folksonomy; Semantic similarity; Social networks},
  document_type   = {Conference Paper},
  journal         = {Komp'juternaja Lingvistika i Intellektual'nye Tehnologii},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84952760170&partnerID=40&md5=01cfd5eb9ff3e0c2f59dd68ebc8831f2},
}

@Article{ley_seitlinger_2015,
  author          = {Ley, T. and Seitlinger, P.},
  title           = {Dynamics of human categorization in a collaborative tagging system: How social processes of semantic stabilization shape individual sensemaking},
  journal         = {Computers in Human Behavior},
  year            = {2015},
  volume          = {51},
  number          = {PA},
  pages           = {140-151},
  issn            = {0747-5632},
  note            = {cited By},
  abstract        = {We study how categories form and develop over time in a sensemaking task by groups of students employing a collaborative tagging system. In line with distributed cognition theories, we look at both the tags students use and their strength of representation in memory. We hypothesize that categories get more differentiated over time as students learn, and that semantic stabilization on the group level (i.e. the convergence in the use of tags) mediates this relationship. Results of a field experiment that tested the impact of topic study duration on the specificity of tags confirms these hypotheses, although it was not study duration that produced this effect, but rather the effectiveness of the collaborative taxonomy the groups built. In the groups with higher levels of semantic stabilization, we found use of more specific tags and better representation in memory. We discuss these findings with regard to the important role of the information value of tags that would drive both the convergence on the group level as well as a shift to more specific levels of categorization. We also discuss the implication for cognitive science research by highlighting the importance of collaboratively built artefacts in the process of how knowledge is acquired, and implications for educational applications of collaborative tagging environments. © 2015 Published by Elsevier Ltd.},
  affiliation     = {Tallinn University, Tallinn, Estonia; Graz University of Technology, Graz, Austria},
  author_keywords = {Categorization; Collaborative tagging; Distributed cognition; Sensemaking; Social web},
  document_type   = {Article},
  doi             = {10.1016/j.chb.2015.04.053},
  keywords        = {Categorization, Sensemaking, Collaborative tagging, Distributed cognition, Social web, rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84929573561&doi=10.1016%2fj.chb.2015.04.053&partnerID=40&md5=97bda05ffce2f794517345161ca07248},
}

@Article{cattuto_etal_2007,
  author    = {Ciro Cattuto and Andrea Baldassarri and Vito Domenico Pietro Servedio and Vittorio Loreto},
  title     = {Vocabulary growth in collaborative tagging systems},
  journal   = {CoRR},
  year      = {2007},
  volume    = {abs/0704.3316},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-0704-3316},
  timestamp = {Wed, 07 Jun 2017 14:40:28 +0200},
  url       = {http://arxiv.org/abs/0704.3316},
}

@InProceedings{jin_nakayama_2016,
  author    = {Jiren Jin and H. Nakayama},
  title     = {Annotation order matters: Recurrent Image Annotator for arbitrary length image tagging},
  booktitle = {2016 23rd International Conference on Pattern Recognition (ICPR)},
  year      = {2016},
  pages     = {2452-2457},
  month     = {Dec},
  abstract  = {Automatic image annotation has been an important research topic in facilitating large scale image management and retrieval. Existing methods focus on learning image-tag correlation or correlation between tags to improve annotation accuracy. However, most of these methods evaluate their performance using top-k retrieval performance, where k is fixed. Although such setting gives convenience for comparing different methods, it is not the natural way that humans annotate images. The number of annotated tags should depend on image contents. Inspired by the recent progress in machine translation and image captioning, we propose a novel Recurrent Image Annotator (RIA) model that forms image annotation task as a sequence generation problem so that RIA can natively predict the proper length of tags according to image contents. We evaluate the proposed model on various image annotation datasets. In addition to comparing our model with existing methods using the conventional top-k evaluation measures, we also provide our model as a high quality baseline for the arbitrary length image tagging task. Moreover, the results of our experiments show that the order of tags in training phase has a great impact on the final annotation performance.},
  doi       = {10.1109/ICPR.2016.7900004},
  keywords  = {image annotation;image retrieval;language translation;RIA;annotation accuracy improvement;annotation order matters;arbitrary length image tagging task;automatic image annotation;human annotate images;image annotation datasets;image annotation task;image captioning;image contents;large-scale image management;large-scale image retrieval;learning image-tag correlation;machine translation;recurrent image annotator;sequence generation problem;Correlation;Feature extraction;Indexes;Predictive models;Recurrent neural networks;Training},
}

@Article{goy_etal_2016,
  author   = {Anna Goy and Diego Magro and Giovanna Petrone and Claudia Picardi and Marino Segnan},
  title    = {Ontology-driven collaborative annotation in shared workspaces},
  journal  = {Future Generation Computer Systems},
  year     = {2016},
  volume   = {54},
  pages    = {435 - 449},
  issn     = {0167-739X},
  doi      = {http://dx.doi.org/10.1016/j.future.2015.04.013},
  keywords = {Online collaboration, Collaborative semantic annotation, Ontologies, Knowledge representation, Knowledge sharing, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167739X15001053},
}

@Article{hajibayova_jacob_2016,
  author   = {Lala Hajibayova and Elin K. Jacob},
  title    = {An investigation of the levels of abstraction of tags across three resource genres},
  journal  = {Information Processing \& Management},
  year     = {2016},
  volume   = {52},
  number   = {6},
  pages    = {1178 - 1187},
  issn     = {0306-4573},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2016.05.005},
  keywords = {Genre, User-generated vocabulary, Tagging, Level of abstraction of tags, Basic level categories},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457316301364},
}

@Article{nakatsuji_etal_2016,
  author   = {Makoto Nakatsuji and Hiroyuki Toda and Hiroshi Sawada and Jin Guang Zheng and James A. Hendler},
  title    = {Semantic sensitive tensor factorization},
  journal  = {Artificial Intelligence},
  year     = {2016},
  volume   = {230},
  pages    = {224 - 245},
  issn     = {0004-3702},
  doi      = {http://dx.doi.org/10.1016/j.artint.2015.09.001},
  keywords = {Tensor factorization, Linked open data, Recommendation systems, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S000437021500137X},
}

@Article{li_etal_2016b,
  author   = {Yibai Li and Xuequn Wang and Xiaolin Lin and Mohammad Hajli},
  title    = {Seeking and sharing health information on social media: A net valence model and cross-cultural comparison},
  journal  = {Technological Forecasting and Social Change},
  year     = {2016},
  issn     = {0040-1625},
  doi      = {http://dx.doi.org/10.1016/j.techfore.2016.07.021},
  keywords = {Health information, Social network sites, Social media, Net valence model},
  url      = {http://www.sciencedirect.com/science/article/pii/S0040162516301780},
}

@Article{zhang_etal_2016,
  author   = {L. Zhang and Z. Cai and X. Wang},
  title    = {FakeMask: A Novel Privacy Preserving Approach for Smartphones},
  journal  = {IEEE Transactions on Network and Service Management},
  year     = {2016},
  volume   = {13},
  number   = {2},
  pages    = {335-348},
  month    = {June},
  issn     = {1932-4537},
  abstract = {Users can enjoy personalized services provided by various context-aware applications that collect users' contexts through sensor-equipped smartphones. Meanwhile, serious privacy concerns arise due to the lack of privacy preservation mechanisms. Currently, most mechanisms apply passive defense policies in which the released contexts from a privacy preservation system are always real, leading to a great probability with which an adversary infers the hidden sensitive contexts about the users. In this paper, we apply a deception policy for privacy preservation and present a novel technique, FakeMask, in which fake contexts may be released to provably preserve users' privacy. The output sequence of contexts by FakeMask can be accessed by the untrusted context-aware applications or be used to answer queries from those applications. Since the output contexts may be different from the original contexts, an adversary has greater difficulty in inferring the real contexts. Therefore, FakeMask limits what adversaries can learn from the output sequence of contexts about the user being in sensitive contexts, even if the adversaries are powerful enough to have the knowledge about the system and the temporal correlations among the contexts. The essence of FakeMask is a privacy checking algorithm which decides whether to release a fake context for the current context of the user. We present a novel privacy checking algorithm and an efficient one to accelerate the privacy checking process. Extensive evaluation experiments on real smartphone context traces of users demonstrate the improved performance of FakeMask over other approaches.},
  doi      = {10.1109/TNSM.2016.2559448},
  keywords = {data privacy;mobile computing;security of data;smart phones;FakeMask;context-aware application;privacy checking algorithm;smart phone;user privacy preservation mechanism;Context;Context modeling;Context-aware services;Correlation;Markov processes;Privacy;Smart phones;Privacy protection;privacy protection;semi-Markov model;service},
}

@Book{lorince_todd_2016,
  title         = {Music tagging and listening: Testing the memory cue hypothesis in a collaborative tagging system},
  year          = {2016},
  author        = {Lorince, J. and Todd, P.M.},
  note          = {cited By},
  abstract      = {As an example of exploring human memory cue use in an ecologically valid context, we present ongoing work to examine the “memory cue hypothesis” in collaborative tagging. In collaborative tagging systems, which allow users to assign freeform textual labels to digital resources, it is generally assumed that tags function as memory cues that facilitate future retrieval of the resources to which they are assigned. There is, however, little empirical evidence demonstrating that this is in fact the case. Employing large-scale music listening and tagging data from the social music website Last.fm as a case study, we present a set of time series and information theoretic analytic methods we are using to explore how patterns of content tagging and interaction support or refute the hypothesis that tags function as retreival cues. Early results are, on average, consistent with the hypothesis. There is an immediate practical application of this work to those working with collaborative tagging systems (are user motivations what we think they are?), but our work also comprises contributions of interest to the cognitive science community: First, we are expanding our understanding of how people generate and use memory cues “in the wild.” Second, we are enriching the “toolbox” available to cognitive scientists for studying cognition using large-scale, ecologically valid data that is latent in the logged activity of web users. © 2017 Taylor \& Francis.},
  affiliation   = {Northwestern University, United States; Indiana University, United States},
  document_type = {Book Chapter},
  doi           = {10.4324/9781315413570},
  journal       = {Big Data in Cognitive Science},
  pages         = {117-143},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85020807801&doi=10.4324%2f9781315413570&partnerID=40&md5=b6811228604393de883531d1de31e9e6},
}

@Article{wang_etal_2014,
  author    = {Peng Wang and Baowen Xu and Yurong Wu and Xiaoyu Zhou},
  title     = {Link Prediction in Social Networks: the State-of-the-Art},
  journal   = {CoRR},
  year      = {2014},
  volume    = {abs/1411.5118},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/WangXWZ14},
  keywords  = {rank1},
  timestamp = {Wed, 07 Jun 2017 14:41:12 +0200},
  url       = {http://arxiv.org/abs/1411.5118},
}

@InProceedings{borrego_etal_2017,
  author    = {G. Borrego and A. L. Morán and R. Palacio},
  title     = {Preliminary Evaluation of a Tag-Based Knowledge Condensation Tool in Agile and Distributed Teams},
  booktitle = {2017 IEEE 12th International Conference on Global Software Engineering (ICGSE)},
  year      = {2017},
  pages     = {51-55},
  month     = {May},
  abstract  = {An important challenge in Agile Global Software Development (AGSD) is architectural knowledge vaporization, i.e., the loss of technical knowledge due to a lack of documentation. In a previous work we identified that technical knowledge is usually available in unstructured textual electronic means (e.g. chat, mail, blogs, etc. - also known as UTEMs) used by AGSD workers during development, although not necessarily easily accessible. Also, we proposed the concept of Knowledge Condensation as a means to recover knowledge from UTEMs, by introducing a structure to their contents along with means to retrieve knowledge. In this work we present our first step towards a Knowledge Condensation tool, through a prototype tagging mechanism to structure architectural knowledge during UTEM interactions. We evaluated the prototype and obtained the following results: (1) a high perception of usefulness and a high projected intention of use, and (2) the identification of 6 tagging behavior profiles from participants. These tagging behaviors can be grouped as (i) participants who tag as expected, (ii) participants who tag more than expected, and (ii) participants who tag less than expected. These results suggest that it is important to cope with "little or no tagging" and "excessive tagging" behaviors to implement knowledge condensation based on UTEM tagging. Furthermore, we identified that tagging could be a good option to structure UTEM interactions, and to retrieve architectural knowledge from UTEM logs. Future work includes improving the tagging mechanism prototype based on the participants' suggestions and overall evaluation results, and implementing a searcher prototype to further evaluate the concept of knowledge condensation based on tagging of UTEM interactions.},
  doi       = {10.1109/ICGSE.2017.14},
  keywords  = {software prototyping;text analysis;AGSD;UTEM interactions;UTEM logs;UTEM tagging;agile global software development;agile-and-distributed teams;architectural knowledge retrieval;architectural knowledge vaporization;tag-based knowledge condensation tool;tagging behavior profiles;tagging mechanism prototype;unstructured textual electronic means;Companies;Documentation;Prototypes;Tagging;Tools;Usability;agile global software development;architectural knowledge;knowledge condensing;knowledge vaporization;unstructured and textual electronic media},
}

@Article{moutafis_etal_2017,
  author   = {P. Moutafis and M. Leng and I. A. Kakadiaris},
  title    = {An Overview and Empirical Comparison of Distance Metric Learning Methods},
  journal  = {IEEE Transactions on Cybernetics},
  year     = {2017},
  volume   = {47},
  number   = {3},
  pages    = {612-625},
  month    = {March},
  issn     = {2168-2267},
  abstract = {In this paper, we first offer an overview of advances in the field of distance metric learning. Then, we empirically compare selected methods using a common experimental protocol. The number of distance metric learning algorithms proposed keeps growing due to their effectiveness and wide application. However, existing surveys are either outdated or they focus only on a few methods. As a result, there is an increasing need to summarize the obtained knowledge in a concise, yet informative manner. Moreover, existing surveys do not conduct comprehensive experimental comparisons. On the other hand, individual distance metric learning papers compare the performance of the proposed approach with only a few related methods and under different settings. This highlights the need for an experimental evaluation using a common and challenging protocol. To this end, we conduct face verification experiments, as this task poses significant challenges due to varying conditions during data acquisition. In addition, face verification is a natural application for distance metric learning because the encountered challenge is to define a distance function that: 1) accurately expresses the notion of similarity for verification; 2) is robust to noisy data; 3) generalizes well to unseen subjects; and 4) scales well with the dimensionality and number of training samples. In particular, we utilize well-tested features to assess the performance of selected methods following the experimental protocol of the state-of-the-art database labeled faces in the wild. A summary of the results is presented along with a discussion of the insights obtained and lessons learned by employing the corresponding algorithms.},
  doi      = {10.1109/TCYB.2016.2521767},
  keywords = {face recognition;learning (artificial intelligence);distance function;distance metric learning;face recognition;face verification;similarity learning;Cybernetics;Face;Face recognition;Learning systems;Measurement;Protocols;Taxonomy;Dimensionality reduction;distance metric learning;face recognition;face verification;similarity learning},
}

@Article{lee_brusilovsky_2017,
  author   = {Danielle H. Lee and Peter Brusilovsky},
  title    = {How to measure information similarity in online social networks: A case study of Citeulike},
  journal  = {Information Sciences},
  year     = {2017},
  volume   = {418},
  pages    = {46 - 60},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2017.07.034},
  keywords = {Information similarity, Online social networks, Watching relations, Group membership, Social tags, Citeulike},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025517308538},
}

@Article{wu_etal_2017a,
  author   = {Wenmin Wu and Jianli Zhao and Chunsheng Zhang and Fang Meng and Zeli Zhang and Yang Zhang and Qiuxia Sun},
  title    = {Improving performance of tensor-based context-aware recommenders using Bias Tensor Factorization with context feature auto-encoding},
  journal  = {Knowledge-Based Systems},
  year     = {2017},
  volume   = {128},
  pages    = {71 - 77},
  issn     = {0950-7051},
  doi      = {http://dx.doi.org/10.1016/j.knosys.2017.04.011},
  keywords = {Context-aware recommendation, Tensor factorization, Regression tree, Context features selection},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705117301909},
}

@Article{chen_etal_2017b,
  author   = {Jinpeng Chen and Yu Liu and Guang Yang and Ming Zou},
  title    = {Inferring Tag Co-occurrence Relationship across Heterogeneous Social Networks},
  journal  = {Applied Soft Computing},
  year     = {2017},
  issn     = {1568-4946},
  doi      = {http://dx.doi.org/10.1016/j.asoc.2017.07.029},
  keywords = {Tag Co-occurrence, Link prediction, heterogeneous network, Flickr, weight path},
  url      = {http://www.sciencedirect.com/science/article/pii/S1568494617304404},
}

@Article{deng_etal_2017,
  author   = {Xiaofang Deng and Yuansheng Zhong and Linyuan Lü and Naixue Xiong and Chiho Yeung},
  title    = {A general and effective diffusion-based recommendation scheme on coupled social networks},
  journal  = {Information Sciences},
  year     = {2017},
  volume   = {417},
  pages    = {420 - 434},
  issn     = {0020-0255},
  doi      = {http://dx.doi.org/10.1016/j.ins.2017.07.021},
  keywords = {Social information, Cold start, Diffusion-based recommendation, Mass diffusion},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025517308368},
}

@Article{li_etal_2017,
  author   = {Jianguo Li and Yong Tang and Jiemin Chen},
  title    = {Leveraging tagging and rating for recommendation: RMF meets weighted diffusion on tripartite graphs},
  journal  = {Physica A: Statistical Mechanics and its Applications},
  year     = {2017},
  volume   = {483},
  pages    = {398 - 411},
  issn     = {0378-4371},
  doi      = {http://dx.doi.org/10.1016/j.physa.2017.04.121},
  keywords = {Regularized matrix factorization, Collaborative filtering, Tag, Diffusion, Tripartite graphs},
  url      = {http://www.sciencedirect.com/science/article/pii/S0378437117304107},
}

@Article{2015IEITI..98..902M,
  author  = {{Ma}, T. and {Zhou}, J. and {Tang}, M. and {Tian}, Y. and {Al-Dhelaan}, A. and {Al-Rodhaan}, M. and {Lee}, S.},
  title   = {{Social Network and Tag Sources Based Augmenting Collaborative Recommender System}},
  journal = {IEICE Transactions on Information and Systems},
  year    = {2015},
  volume  = {98},
  pages   = {902-910},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System},
  adsurl  = {http://adsabs.harvard.edu/abs/2015IEITI..98..902M},
  doi     = {10.1587/transinf.2014EDP7283},
}

@Article{arnaboldi_etal_2017,
  author   = {Valerio Arnaboldi and Mattia G. Campana and Franca Delmastro and Elena Pagani},
  title    = {A personalized recommender system for pervasive social networks},
  journal  = {Pervasive and Mobile Computing},
  year     = {2017},
  volume   = {36},
  pages    = {3 - 24},
  issn     = {1574-1192},
  note     = {Special Issue on Pervasive Social Computing},
  doi      = {http://dx.doi.org/10.1016/j.pmcj.2016.08.010},
  keywords = {Pervasive content sharing, Mobile social networks, Opportunistic networks, Personalized recommender systems},
  url      = {http://www.sciencedirect.com/science/article/pii/S1574119216301365},
}

@Article{najafabadi_etal_2017,
  author   = {Maryam Khanian Najafabadi and Mohd Naz'ri Mahrin and Suriayati Chuprat and Haslina Md Sarkan},
  title    = {Improving the accuracy of collaborative filtering recommendations using clustering and association rules mining on implicit data},
  journal  = {Computers in Human Behavior},
  year     = {2017},
  volume   = {67},
  pages    = {113 - 128},
  issn     = {0747-5632},
  doi      = {http://dx.doi.org/10.1016/j.chb.2016.11.010},
  keywords = {Recommender systems, Collaborative filtering, Sparsity problem, Association rules mining, Clustering, Implicit feedback},
  url      = {http://www.sciencedirect.com/science/article/pii/S0747563216307518},
}

@Article{mezghani_etal_2017,
  author   = {Manel Mezghani and André Péninou and Corinne Amel Zayani and Ikram Amous and Florence Sèdes},
  title    = {Producing relevant interests from social networks by mining users' tagging behaviour: A first step towards adapting social information},
  journal  = {Data \& Knowledge Engineering},
  year     = {2017},
  volume   = {108},
  pages    = {15 - 29},
  issn     = {0169-023X},
  doi      = {http://dx.doi.org/10.1016/j.datak.2016.12.003},
  keywords = {User interests, Tagging behaviour, Resource, Indexation, Social network, Adaptation, Indexing methods, Semi-structured data and XML},
  url      = {http://www.sciencedirect.com/science/article/pii/S0169023X16303718},
}

@Article{ji_shen_2015,
  author   = {Ke Ji and Hong Shen},
  title    = {Addressing cold-start: Scalable recommendation with tags and keywords},
  journal  = {Knowledge-Based Systems},
  year     = {2015},
  volume   = {83},
  pages    = {42 - 50},
  issn     = {0950-7051},
  abstract = {Abstract Cold start problem for new users and new items is a major challenge facing most collaborative filtering systems. Existing methods to collaborative filtering (CF) emphasize to scale well up to large and sparse dataset, lacking of scalable approach to dealing with new data. In this paper, we consider a novel method for alleviating the problem by incorporating content-based information about users and items, i.e., tags and keywords. The user-item ratings imply the relevance of users’ tags to items’ keywords, so we convert the direct prediction on the user-item rating matrix into the indirect prediction on the tag-keyword relation matrix that adopts to the emergence of new data. We first propose a novel neighborhood approach for building the tag-keyword relation matrix based on the statistics of tag-keyword pairs in the ratings. Then, with the relation matrix, we propose a 3-factor matrix factorization model over the rating matrix, for learning every user’s interest vector for selected tags and every item’s correlation vector for extracted keywords. Finally, we integrate the relation matrix with the two kinds of vectors to make recommendations. Experiments on real dataset demonstrate that our method not only outperforms other state-of-the-art \{CF\} algorithms for historical data, but also has good scalability for new data. },
  doi      = {https://doi.org/10.1016/j.knosys.2015.03.008},
  keywords = {Recommender systems, Matrix factorization, Tag-keyword, Cold start, Scalability},
  url      = {http://www.sciencedirect.com/science/article/pii/S0950705115001008},
}

@Article{Kalantidis2011,
  author   = {Kalantidis, Yannis and Tolias, Giorgos and Avrithis, Yannis and Phinikettos, Marios and Spyrou, Evaggelos and Mylonas, Phivos and Kollias, Stefanos},
  title    = {VIRaL: Visual Image Retrieval and Localization},
  journal  = {Multimedia Tools and Applications},
  year     = {2011},
  volume   = {51},
  number   = {2},
  pages    = {555--592},
  month    = {Jan},
  issn     = {1573-7721},
  abstract = {New applications are emerging every day exploiting the huge data volume in community photo collections. Most focus on popular subsets, e.g., images containing landmarks or associated to Wikipedia articles. In this work we are concerned with the problem of accurately finding the location where a photo is taken without needing any metadata, that is, solely by its visual content. We also recognize landmarks where applicable, automatically linking them to Wikipedia. We show that the time is right for automating the geo-tagging process, and we show how this can work at large scale. In doing so, we do exploit redundancy of content in popular locations---but unlike most existing solutions, we do not restrict to landmarks. In other words, we can compactly represent the visual content of all thousands of images depicting e.g., the Parthenon and still retrieve any single, isolated, non-landmark image like a house or a graffiti on a wall. Starting from an existing, geo-tagged dataset, we cluster images into sets of different views of the same scene. This is a very efficient, scalable, and fully automated mining process. We then align all views in a set to one reference image and construct a 2D scene map. Our indexing scheme operates directly on scene maps. We evaluate our solution on a challenging one million urban image dataset and provide public access to our service through our online application, VIRaL.},
  day      = {01},
  doi      = {10.1007/s11042-010-0651-7},
  url      = {https://doi.org/10.1007/s11042-010-0651-7},
}

@Article{kim_etal_2010,
  author   = {Heung-Nam Kim and Ae-Ttie Ji and Inay Ha and Geun-Sik Jo},
  title    = {Collaborative filtering based on collaborative tagging for enhancing the quality of recommendation},
  journal  = {Electronic Commerce Research and Applications},
  year     = {2010},
  volume   = {9},
  number   = {1},
  pages    = {73 - 83},
  issn     = {1567-4223},
  note     = {Special Issue: Social Networks and Web 2.0},
  abstract = {We propose a collaborative filtering method to provide an enhanced recommendation quality derived from user-created tags. Collaborative tagging is employed as an approach in order to grasp and filter users’ preferences for items. In addition, we explore several advantages of collaborative tagging for data sparseness and a cold-start user. These applications are notable challenges in collaborative filtering. We present empirical experiments using a real dataset from del.icio.us. Experimental results show that the proposed algorithm offers significant advantages both in terms of improving the recommendation quality for sparse data and in dealing with cold-start users as compared to existing work.},
  doi      = {http://dx.doi.org/10.1016/j.elerap.2009.08.004},
  keywords = {Collaborative tagging, Collaborative filtering, Recommender system},
  url      = {http://www.sciencedirect.com/science/article/pii/S1567422309000544},
}

@InProceedings{mustafa_etal_2017,
  author    = {N. Mustafa and A. O. Ibrahim and A. Ahmed and A. Abdullah},
  title     = {Collaborative filtering: Techniques and applications},
  booktitle = {2017 International Conference on Communication, Control, Computing and Electronics Engineering (ICCCCEE)},
  year      = {2017},
  pages     = {1-6},
  month     = {Jan},
  abstract  = {During the last decade a huge amount of data have been shown and introduced in the Internet. Recommender systems are thus predicting the rating that a user would give to an item. Collaborative filtering (CF) techniques are the most popular and widely used by recommender systems technique, which utilize similar neighbors to generate recommendations. This paper provides the concepts, methods, applications and evaluations of the CF based on the literature review. The paper also highlights the discussion of the types of the recommender systems as general and types of CF such as; memory based, model based and hybrid model. In addition, this paper discusses how to choose an appropriate type of CF. The evaluation methods of the CF systems are also provided throughout the paper.},
  doi       = {10.1109/ICCCCEE.2017.7867668},
  keywords  = {Internet;collaborative filtering;recommender systems;CF techniques;Internet;collaborative filtering;hybrid model CF;memory based CF;model based CF;recommender systems;Collaboration;Information filters;Measurement;Collaborative filtering;Evaluation Metrics;hybrid model;memory based;model based;recommender systems},
}

@InProceedings{chen_etal_2017,
  author    = {C. Chen and Z. Xing and X. Wang},
  title     = {Unsupervised Software-Specific Morphological Forms Inference from Informal Discussions},
  booktitle = {2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE)},
  year      = {2017},
  pages     = {450-461},
  month     = {May},
  abstract  = {Informal discussions on social platforms (e.g., Stack Overflow) accumulates a large body of programming knowledge in natural language text. Natural language process (NLP) techniques can be exploited to harvest this knowledge base for software engineering tasks. To make an effective use of NLP techniques, consistent vocabulary is essential. Unfortunately, the same concepts are often intentionally or accidentally mentioned in many different morphological forms in informal discussions, such as abbreviations, synonyms and misspellings. Existing techniques to deal with such morphological forms are either designed for general English or predominantly rely on domain-specific lexical rules. A thesaurus of software-specific terms and commonly-used morphological forms is desirable for normalizing software engineering text, but very difficult to build manually. In this work, we propose an automatic approach to build such a thesaurus. Our approach identifies software-specific terms by contrasting software-specific and general corpuses, and infers morphological forms of software-specific terms by combining distributed word semantics, domain-specific lexical rules and transformations, and graph analysis of morphological relations. We evaluate the coverage and accuracy of the resulting thesaurus against community-curated lists of software-specific terms, abbreviations and synonyms. We also manually examine the correctness of the identified abbreviations and synonyms in our thesaurus. We demonstrate the usefulness of our thesaurus in a case study of normalizing questions from Stack Overflow and CodeProject.},
  doi       = {10.1109/ICSE.2017.48},
  keywords  = {Dictionaries;Electronic publishing;Encyclopedias;Internet;Software engineering;Thesauri;Stack Overflow;abbreviation;morphological form;synonym;word embedding, rank2},
}

@Article{zhou_etal_2017,
  author          = {Zhou, D. and Wu, X. and Zhao, W. and Lawless, S. and Liu, J.},
  title           = {Exploring external knowledge base for personalized search in collaborative tagging systems},
  journal         = {Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST},
  year            = {2017},
  volume          = {201},
  pages           = {408-417},
  note            = {cited By},
  abstract        = {Alongside the enormous volume of user-generated content posted to World Wide Web, there exists a thriving demand for search personalization services, especially those utilizing collaborative tagging data. To provide personalized services, a user model is usually required. We address the setting adopted by the majority of previous work, where a user model consists solely of the user’s past information. We construct an augmented user model from a number of tags and documents. These resources are further processed according to the user’s past information by exploring external knowledge base. A novel generative model is proposed for user model generation. This model leverages recent advances in neural language models such as Word Embeddings with latent semantic models such as Latent Dirichlet Allocation. We further present a new query expansion method to facilitate the desired personalized retrieval. Experiments conducted by utilizing real-world collaborative tagging data show that the methods proposed in the current paper outperform several non-personalized methods as well as existing personalized search methods by utilizing user models solely constructed from usage histories. © 2017, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.},
  affiliation     = {School of Computer Science and Engineering, Hunan University of Science and Technology, Xiangtan, Hunan, China; ADAPT Centre, Knowledge and Date Engineering Group, School of Computer Science and Statistics, Trinity College Dublin, Dublin 2, Ireland},
  author_keywords = {Collaborative tagging systems; Latent semantic models; Personalized search; Query expansion; Word embeddings},
  document_type   = {Book Chapter},
  doi             = {10.1007/978-3-319-59288-6_37},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85021907207&doi=10.1007%2f978-3-319-59288-6_37&partnerID=40&md5=ffef3abfc73a7111bdfcfe1a632043df},
}

@Article{yin_etal_2007,
  author        = {Yin, P. and Zhang, M. and Li, X.},
  title         = {Recommending scientific literatures in a collaborative tagging environment},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2007},
  volume        = {4822 LNCS},
  pages         = {478-481},
  note          = {cited By},
  abstract      = {Recently, collaborative tagging has become popular in the web2.0 world. Tags can be helpful if used for the recommendation since they reflect characteristic content features of the resources. However, there are few researches which introduce tags into the recommendation. This paper proposes a tag-based recommendation framework for scientific literatures which models the user interests with tags and literature keywords. A hybrid recommendation algorithm is then applied which is similar to the user-user collaborative filtering algorithm except that the user similarity is measured based on the vector model of user keywords other than the rating matrix, and that the rating is not from the user but represented as user-item similarity computed with the dot-product-based similarity instead of the cosine-based similarity. Experiments show that our tag-based algorithm is better than the baseline algorithm and the extension of user model and dot-product-based similarity computation are also helpful. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation   = {School of Electronics Engineering and Computer, Science Peking University, Beijing, China},
  document_type = {Conference Paper},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-38149029112&partnerID=40&md5=85878767c03acc02f3b4d2febbf0ae67},
}

@InProceedings{firan_etal_2007a,
  author    = {C. S. Firan and W. Nejdl and R. Paiu},
  title     = {The Benefit of Using Tag-Based Profiles},
  booktitle = {2007 Latin American Web Conference (LA-WEB 2007)},
  year      = {2007},
  pages     = {32-41},
  month     = {Oct},
  abstract  = {Collaborative tagging, i.e. the process of assigning metadata in the form of keywords to shared content by many users, has emerged as an important way to provide information about resources on the Web and elsewhere. Such keywords (tags) are used to enable the organization of information within personal information spaces, such as photo collections, but can also be shared, allowing browsing and searching with the help of tags attached by other users to information resources from the Web. Recent research has shown that such tag distributions stabilize over time and can be used to improve search on the Web. In this paper we are interested in another aspect, namely how they characterize the user and enable personalized recommendations. Using data from a frequently used music search portal, Last.fm, we analyze tag usage and statistics and investigate the use of tag-based user profiles in contrast to conventional user profiles based on song and track usage. We specify recommendation algorithms based on tag user profiles, and explore how collaborative filtering recommendations based on these tag profiles are different from recommendations based on song/track profiles. Finally, we describe a new search-based method, which uses tags to recommend songs interesting to a user, yielding substantially improved results. The paper finishes with a discussion of some future work to further improve tag-based search and recommendation in community Web sites.},
  doi       = {10.1109/LA-Web.2007.13},
  keywords  = {Internet;information resources;user interfaces;collaborative tagging;community Web sites;information resources;metadata;search-based method;shared content;tag-based profiles;Collaboration;Collaborative work;Filtering algorithms;Information filtering;Information resources;Portals;Recommender systems;Statistical analysis;Statistical distributions;Tagging},
}

@Book{surowiecki_2004,
  title  = {The Wisdom of Crowds: Why the Many Are Smarter Than the Few and How Collective Wisdom Shapes Business, Economies, Societies and Nations},
  year   = {2004},
  author = {Surowiecki, James},
}

@Conference{halpin_etal_2006,
  author        = {Halpin, H. and Robu, V. and Shepherd, H.},
  title         = {The dynamics and semantics of collaborative tagging},
  year          = {2006},
  volume        = {209},
  note          = {cited By},
  abstract      = {The debate within the Web community over the optimal means by which to organize information often pits formalized classifications against distributed collaborative tagging systems. A number of questions remain unanswered, however, regarding the nature of collaborative tagging systems including the dynamics of such systems and whether coherent classification schemes can emerge from undirected tagging by users. Currently millions of users are using collaborative tagging without centrally organizing principles, and many suspect this exhibits features considered to be indicative of a complex system. If this is the case, it remains to be seem whether collaborative tagging by users over time leads to emergent classification schemes that could be formalized into an ontology usable by the Semantic Web. This paper uses data from "popular" tagged sites on the social bookmarking site del.icio.us to examine the dynamics of such collaborative tagging systems. In particular, we are trying to determine whether the distribution of tag frequencies stabilizes, which indicates a degree of cohesion or consensus among users about the optimal tags to describe particular sites. We use tag co-occurrence networks for a sample domain of tags to analyze the meaning of particular tags given their relationship to other tags and automatically create an ontology. We also produce a generative model of collaborative tagging in order to model and understand some of the basic dynamics behind the process.},
  affiliation   = {University of Edinburgh, 2 Buccleuch Place, Edinburgh, United Kingdom; Dutch Center for Mathematics and Computer Science, Kruislaan 413, Amsterdam, Netherlands; Princeton University, Wallace Hall, Princeton, NJ, United States},
  document_type = {Conference Paper},
  journal       = {CEUR Workshop Proceedings},
  keywords      = {rank2},
  page_count    = {10},
  source        = {Scopus},
  url           = {http://ceur-ws.org/Vol-209/saaw06-full01-halpin.pdf},
}

@InProceedings{giannakidou_etal_2008a,
  author    = {E. Giannakidou and V. Koutsonikola and A. Vakali and Y. Kompatsiaris},
  title     = {Co-Clustering Tags and Social Data Sources},
  booktitle = {2008 The Ninth International Conference on Web-Age Information Management},
  year      = {2008},
  pages     = {317-324},
  month     = {July},
  abstract  = {Under social tagging systems, a typical Web 2.0 application, users label digital data sources by using freely chosen textual descriptions (tags). Poor retrieval in the aforementioned systems remains a major problem mostly due to questionable tag validity and tag ambiguity. Earlier clustering techniques have shown limited improvements, since they were based mostly on tag co-occurrences. In this paper, a co-clustering approach is employed, that exploits joint groups of related tags and social data sources, in which both social and semantic aspects of tags are considered simultaneously. Experimental results demonstrate the efficiency and the beneficial outcome of the proposed approach in correlating relevant tags and resources.},
  doi       = {10.1109/WAIM.2008.61},
  keywords  = {Internet;identification technology;pattern clustering;Web 2.0 application;coclustering approach;social tagging systems;textual descriptions;Bibliographies;Data mining;Data structures;Informatics;Information management;Knowledge representation;Multimedia systems;Ontologies;Tagging;Telematics;Co-clustering;Semantic Similarity;Social Similarity;Social Tagging Systems},
}

@Conference{zhao_etal_2008,
  author          = {Zhao, S. and Du, N. and Nauerz, A. and Zhang, X. and Yuan, Q. and Fu, R.},
  title           = {Improved recommendation based on collaborative tagging behaviors},
  year            = {2008},
  pages           = {413-416},
  note            = {cited By},
  abstract        = {Considering the natural tendency of people to follow direct or indirect cues of other people's activities, collaborative filtering-based recommender systems often predict the utility of an item for a particular user according to previous ratings by other similar users. Consequently, effective searching for the most related neighbors is critical for the success of the recommendations. In recent years, collaborative tagging systems with social bookmarking as their key component from the suite of Web 2.0 technologies allow users to freely bookmark and assign semantic descriptions to various shared resources on the web. While the list of favorite web pages indicates the interests or taste of each user, the assigned tags can further provide useful hints about what a user thinks of the pages. In this paper, we propose a new collaborative filtering approach TBCF (Tag-based Collaborative Filtering) based on the semantic distance among tags assigned by different users to improve the effectiveness of neighbor selection. That is, two users could be considered similar not only if they rated the items similarly, but also if they have similar cognitions over these items. We tested TBCF on real-life datasets, and the experimental results show that our approach has significant improvement against the traditional cosine-based recommendation method while leveraging user input not explicitly targeting the recommendation system. Copyright 2008 ACM.},
  affiliation     = {IBM, China Research Laboratory, Beijing, 100094, China; Beijing University of Posts and Telecommunications, Beijing, 100876, China; IBM, Research and Development, Schoenacher Str. 220, Boeblingen, 71032, Germany},
  art_number      = {1378843},
  author_keywords = {Collaborative filtering; Recommendation; Tag; Web2.0},
  document_type   = {Conference Paper},
  doi             = {10.1145/1378773.1378843},
  journal         = {International Conference on Intelligent User Interfaces, Proceedings IUI},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-54949093673&doi=10.1145%2f1378773.1378843&partnerID=40&md5=483255bbd46c6ef8cc174f50bce55d4f},
}

@Conference{yahia_etal_2008,
  author        = {Yahia, S.A. and Benedikt, M. and Lakshmanan, L.V.S. and Stoyanovichy, J.},
  title         = {Efficient network aware search in collaborative tagging sites},
  year          = {2008},
  volume        = {1},
  number        = {1},
  pages         = {710-721},
  note          = {cited By},
  abstract      = {The popularity of collaborative tagging sites presents a unique opportunity to explore keyword search in a context where query results are determined by the opinion of a network of taggers related to a seeker. In this paper, we present the first in-depth study of network-aware search. We investigate efficient top-k processing when the score of an answer is computed as its popularity among members of a seeker's network. We argue that obvious adaptations of top-k algorithms are too space-intensive, due to the dependence of scores on the seeker's network. We therefore develop algorithms based on maintaining score upper-bounds. The global upper-bound approach maintains a single score upper-bound for every pair of item and tag, over the entire collection of users. The resulting bounds are very coarse. We thus investigate clustering seekers based on similar behavior of their networks. We show that finding the optimal clustering of seekers is intractable, but we provide heuristic methods that give substantial time improvements. We then give an optimization that can benefit smaller populations of seekers based on clustering of taggers. Our results are supported by extensive experiments on del.icio.us datasets. © 2008 VLDB Endowment.},
  affiliation   = {Yahoo Research, United States; Oxford University, United Kingdom; University of British Columbia, Canada; Columbia University, United States; Natural Sciences and Engineering Research Council of Canada, Canada; National Institute of Health, United States},
  document_type = {Article},
  journal       = {Proceedings of the VLDB Endowment},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84859178171&partnerID=40&md5=7ae1d5e57255f5c387a9e3289fd450e1},
}

@Conference{rader_wash_2008,
  author          = {Rader, E. and Wash, R.},
  title           = {Influences on tag choices in del.icio.us},
  year            = {2008},
  pages           = {239-248},
  note            = {cited By},
  abstract        = {Collaborative tagging systems have the potential to produce socially constructed information organization schemes. The effectiveness of tags for finding and re-finding information depends upon how individual users choose tags; however, influences on users' tag choices are poorly understood. We quantitatively test competing hypotheses from the literature concerning these choices, using data from del.icio.us (a collaborative tagging system for organizing web bookmarks) and a computer model of possible tag choice strategies. We find evidence that users choose tags in a pattern consistent with personal information management goals, rather than as a result of social influence. Copyright 2008 ACM.},
  affiliation     = {School of Information, University of Michigan, United States},
  author_keywords = {Collaborative tagging; Computer model; Information management; Logistic regression},
  document_type   = {Conference Paper},
  doi             = {10.1145/1460563.1460601},
  journal         = {Proceedings of the ACM Conference on Computer Supported Cooperative Work, CSCW},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77950856352&doi=10.1145%2f1460563.1460601&partnerID=40&md5=4a175d26aa26f641b712403e1851d032},
}

@InProceedings{wu_etal_2006,
  author    = {Wu, Xian and Zhang, Lei and Yu, Yong},
  title     = {Exploring Social Annotations for the Semantic Web},
  booktitle = {Proceedings of the 15th International Conference on World Wide Web},
  year      = {2006},
  series    = {WWW '06},
  pages     = {417--426},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1135839},
  doi       = {10.1145/1135777.1135839},
  isbn      = {1-59593-323-9},
  keywords  = {emergent semantics, semantic web, social annotation, social bookmarks, rank1},
  location  = {Edinburgh, Scotland},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1135777.1135839},
}

@InProceedings{janik_kochut_2008,
  author    = {M. Janik and K. J. Kochut},
  title     = {Wikipedia in Action: Ontological Knowledge in Text Categorization},
  booktitle = {2008 IEEE International Conference on Semantic Computing},
  year      = {2008},
  pages     = {268-275},
  month     = {Aug},
  abstract  = {We present a new, ontology-based approach to the automatic text categorization. An important and novel aspect of this approach is that our categorization method does not require a training set, which is in contrast to the traditional statistical and probabilistic methods. In the presented method, the ontology, including the domain concepts organized into hierarchies of categories and interconnected by relationships, as well as instances and connections among them, effectively becomes the classifier. Our method focuses on (i) converting a text document into a thematic graph of entities occurring in the document, (ii) ontological classification of the entities in the graph, and (iii) determining the overall categorization of the thematic graph, and as a result, the document itself. In the presented experiments, we used an RDF ontology constructed from the full English version of Wikipedia. Our experiments, conducted on corpora of Reuters news articles, showed that our training-less categorization method achieved a very good overall accuracy.},
  doi       = {10.1109/ICSC.2008.53},
  keywords  = {Web sites;graph theory;ontologies (artificial intelligence);pattern classification;text analysis;RDF ontology;Reuters news articles;Wikipedia;ontological classification;ontological knowledge;text categorization;text document;thematic entities graph;Automotive engineering;Computer science;Distributed computing;Distributed information systems;Information analysis;Large-scale systems;Ontologies;Resource description framework;Text categorization;Wikipedia;ontology;text categorization;wikipedia ontology},
}

@Article{gao_etal_2009,
  author          = {Gao, Q. and Dai, Y. and Fu, K.},
  title           = {Improving personal tagging consistency through visualization of tag relevancy},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5621 LNCS},
  pages           = {326-335},
  note            = {cited By},
  abstract        = {Tagging has emerged as a new means of organizing information, but the inconsistency in tagging behaviors of users is a major drawback which degrades both information organization and retrieval performance. The current study aims to study how the intra-personal consistency of tagging can be improved by proper tag visualization. The effects of visualization of tag frequency and visualization of the relevancy among tags on personal tagging consistency are empirically tested and compared through an experiment with 39 participants. The results show that visualization of tag relevancy improves tagging consistency significantly and reduces mental workload simultaneously; visualization of tag frequency may alleviate perceived physical demand when tag relevancy is visualized. The findings provide clear and meaningful implications for system designers. © 2009 Springer Berlin Heidelberg.},
  affiliation     = {Department of Industrial Engineering, Tsinghua University, Beijing 100084, China},
  author_keywords = {Collaborative tagging systems; Consistency; Information visualization; Tag cloud; Tagging},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-02774-1_36},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-76649121497&doi=10.1007%2f978-3-642-02774-1_36&partnerID=40&md5=7ab96bceb814ac6d945237e26646decc},
}

@Conference{bischoff_etal_2009b,
  author          = {Bischoff, K. and Firan, C.S. and Paiu, R.},
  title           = {Deriving music theme annotations from user tags},
  year            = {2009},
  pages           = {1193-1194},
  note            = {cited By},
  abstract        = {Music theme annotations would be really beneficial for supporting retrieval, but are often neglected by users while annotating. Thus, in order to support users in tagging and to fill the gaps in the tag space, in this paper we develop algorithms for recommending theme annotations. Our methods exploit already existing user tags, the lyrics of music tracks, as well as combinations of both. We compare the results for our recommended theme annotations against genre and style recommendations - a much easier and already studied task. We evaluate the quality of our recommended tags against an expert ground truth data set. Our results are promising and provide interesting insights into possible extensions for music tagging systems to support music search. Copyright is held by the author/owner(s).},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstr. 9a, 30167 Hannover, Germany},
  author_keywords = {Collaborative tagging; High-level music descriptors; Metadata enrichment; Theme tag recommendations},
  document_type   = {Conference Paper},
  doi             = {10.1145/1526709.1526924},
  journal         = {WWW'09 - Proceedings of the 18th International World Wide Web Conference},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70450279739&doi=10.1145%2f1526709.1526924&partnerID=40&md5=ca87c99a5a38c80331c3292264733431},
}

@Article{bischoff_etal_2009a,
  author          = {Bischoff, K. and Firan, C.S. and Kadar, C. and Nejdl, W. and Paiu, R.},
  title           = {Automatically identifying tag types},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5678 LNAI},
  pages           = {31-42},
  note            = {cited By},
  abstract        = {Web 2.0 applications such as delicious, flickr or lastfm have recently become extremely popular and as a result, a large amount of semantically rich metadata produced by users becomes available and exploitable. Tag information can be used for many purposes (e.g. user profiling, recommendations, clustering etc), though the benefit of tags for search is by far the most discussed usage. Tag types differ largely across systems and previous studies showed that, while some tag type categories might be useful for some particular users when searching, they may not bring any benefit to others. The present paper proposes an approach which utilizes rule-based as well as model-based methods, in order to automatically identify exactly these different types of tags. We compare the automatic tag classification produced by our algorithms against a ground truth data set, consisting of manual tag type assignments produced by human raters. Experimental results show that our methods can identify tag types with high accuracy, thus enabling further improvement of systems making use of social tags. © 2009 Springer.},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstrasse 9a, Hannover 30167, Germany},
  author_keywords = {Classification; Collaborative tagging; Social media; Tag types},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-03348-3_7},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350340351&doi=10.1007%2f978-3-642-03348-3_7&partnerID=40&md5=871fd7a3ca26072cefdb867e8571b35a},
}

@Article{bischoff_etal_2009,
  author          = {Bischoff, K. and Firan, C.S. and Georgescu, M. and Nejdl, W. and Paiu, R.},
  title           = {Social knowledge-driven music hit prediction},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2009},
  volume          = {5678 LNAI},
  pages           = {43-54},
  note            = {cited By},
  abstract        = {What makes a song to a chart hit? Many people are trying to find the answer to this question. Previous attempts to identify hit songs have mostly focused on the intrinsic characteristics of the songs, such as lyrics and audio features. As social networks become more and more popular and some specialize on certain topics, information about users' music tastes becomes available and easy to exploit. In the present paper we introduce a new method for predicting the potential of music tracks for becoming hits, which instead of relying on intrinsic characteristics of the tracks directly uses data mined from a music social network and the relationships between tracks, artists and albums. We evaluate the performance of our algorithms through a set of experiments and the results indicate good accuracy in correctly identifying music hits, as well as significant improvement over existing approaches. © 2009 Springer.},
  affiliation     = {L3S Research Center, Leibniz Universität Hannover, Appelstrasse 9a, Hannover 30167, Germany},
  author_keywords = {Classification; Collaborative tagging; Hit songs; Social media},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-03348-3_8},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70350339343&doi=10.1007%2f978-3-642-03348-3_8&partnerID=40&md5=fac26adc2e89cf5cddc965368d275efa},
}

@InProceedings{song_etal_2010,
  author    = {Jian Song and Liang He and Xin Lin},
  title     = {Improving the accuracy of Tagging Recommender System by Using Classification},
  booktitle = {2010 The 12th International Conference on Advanced Communication Technology (ICACT)},
  year      = {2010},
  volume    = {1},
  pages     = {387-391},
  month     = {Feb},
  abstract  = {Collaborative tagging system has become more and more popular and recently achieved widespread success due to flexibility and conceptual comprehensibility of tagging systems. Recommender system has the access to adopt tagging systems to achieve better performance. In this paper we consider that the items can be categorized into different classifications in which users show different interests. Here we adopt a two-step recommender method called TRSUC (Tagging Recommender Systems by Using Classification) which can be described as Inner-Class Recommender or Global Recommender in which we use tag as the intermediary entity between user and item. The experiment using MovieLens as dataset shows that we acquire better results than the recommender algorithms without classifying the items.},
  issn      = {1738-9445},
  keywords  = {groupware;pattern classification;recommender systems;MovieLens dataset;classification;collaborative tagging system;global recommender;inner-class recommender;tagging recommender system;Collaboration;Collaborative work;Filtering algorithms;Helium;Information resources;Information retrieval;Merchandise;Motion pictures;Recommender systems;Tagging;Classification;Collaborative Tagging;Recommender System},
}

@InProceedings{kammergruber_etal_2010,
  author    = {W. C. Kammergruber and M. Viermetz and K. Ehms and M. Langen},
  title     = {Using association rules for discovering tag bundles in social tagging data},
  booktitle = {2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)},
  year      = {2010},
  pages     = {414-419},
  month     = {Oct},
  abstract  = {Social tagging has become very popular with the rise of Web 2.0, making it an important piece of the puzzle that makes up the web phenomenon. In contrast to more elaborate ways of organizing resources, such as taxonomies or ontologies, tagging is very easy to use and understand. Because of its simplicity tagging does not create explicit, formalized, structures. By applying a well-established data mining algorithm we show how common structures in form of tag bundles (a set of related tags) can automatically be derived from a bag of tags. Based on these computed tag bundles tagging applications can assist users with improved user interfaces by introducing user based semantic labelling. For navigating and searching his or her tagged information space there are certainly better user interface elements possible than a tag cloud or a plain list of tags ordered by frequency. We propose that combining frequently associated tags into a tag bundle is such a way. We have tested our approach on a data set gained from Delicious and evaluated it by utilizing the Normalized Google Distance.},
  doi       = {10.1109/CISIM.2010.5643503},
  keywords  = {data mining;identification technology;social networking (online);user interfaces;Web 2.0;association rules;data mining algorithm;normalized Google distance;ontologies;social tagging data;tag bundles;tag bundles tagging applications;taxonomies;user based semantic labelling;user interface elements;Association rules;Context;Google;Organizing;Semantics;Tagging;association rule mining;data mining;folksonomy;personal information management;social tagging;tag bundle},
}

@InProceedings{giannakidou_etal_2010,
  author    = {E. Giannakidou and V. Koutsonikola and A. Vakali and I. Kompatsiaris},
  title     = {Exploring temporal aspects in user-tag co-clustering},
  booktitle = {11th International Workshop on Image Analysis for Multimedia Interactive Services WIAMIS 10},
  year      = {2010},
  pages     = {1-4},
  month     = {April},
  abstract  = {Tagging environments have become an interesting topic of research lately, focused mainly on clustering approaches, in order to extract emergent patterns that are derived from tag similarity and involve tag relations or user interconnections. Apart from tag similarity, an interesting parameter to be analyzed during the clustering/mining process in such data is the actual time that each tagging activity occurred. Indeed, holding a temporal dimension unfolds macroscopic and microscopic views of tagging, highlights links between objects for specific time periods and, in general, lets us observe how the users' tagging activity changes over time. In this article, we propose a time-aware user/tag clustering approach, which groups together similar users and tags that are very “active” during the same time periods. Emphasis is given on using varying time scales, so that we distinguish between clusters that are robust at many time scales and clusters that are somehow occasional, i.e. they emerge, only at a specific time period.},
  issn      = {2158-5873},
  keywords  = {identification technology;pattern clustering;user interfaces;tagging environments;temporal aspects;time-aware user/tag clustering;user interconnections;user-tag co-clustering;Media;Technical Activities Guide - TAG},
  url       = {http://ieeexplore.ieee.org/document/5617671/},
}

@InProceedings{krause_etal_2008,
  author    = {Krause, Beate and Schmitz, Christoph and Hotho, Andreas and Stumme, Gerd},
  title     = {The Anti-social Tagger: Detecting Spam in Social Bookmarking Systems},
  booktitle = {Proceedings of the 4th International Workshop on Adversarial Information Retrieval on the Web},
  year      = {2008},
  series    = {AIRWeb '08},
  pages     = {61--68},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1451998},
  doi       = {10.1145/1451983.1451998},
  isbn      = {978-1-60558-159-0},
  keywords  = {folksonomy, spam detection, web 2.0},
  location  = {Beijing, China},
  numpages  = {8},
  url       = {http://doi.acm.org/10.1145/1451983.1451998},
}

@Conference{ley_seitlinger_2010,
  author          = {Ley, T. and Seitlinger, P.},
  title           = {A cognitive perspective on emergent semantics in collaborative tagging: The basic level effect},
  year            = {2010},
  volume          = {590},
  pages           = {13-18},
  note            = {cited By},
  abstract        = {Researching the emergence of semantics in social systems needs to take into account how users process information in their cognitive system. We report results of an experimental study in which we examined the interaction between individual expertise and the basic level advantage in collaborative tagging. The basic level advantage describes availability in memory of certain preferred levels of taxonomic abstraction when categorizing objects and has been shown to vary with level of expertise. In the study, groups of students tagged internet resources for a 10-week period. We measured the availability of tags in memory with an association test and a relevance rating and found a basic level advantage for tags from more general as opposed to specific levels of the taxonomy. An interaction with expertise also emerged. Contrary to our expectations, groups that spent less time to develop a shared understanding shifted to more specific levels as compared to groups that spent more time on a topic. We attribute this to impaired collaboration in the groups. We discuss implications for personalized tag and resource recommendations.},
  affiliation     = {Know-Center, Inffeldgasse 21a, 8010 Graz, Austria; Cognitive Science Section, University of Graz, Universitätsplatz 2, 8010 Graz, Austria},
  author_keywords = {Categorization; Personalized recommendation; Tagging},
  document_type   = {Conference Paper},
  journal         = {CEUR Workshop Proceedings},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://ceur-ws.org/Vol-590/sasweb10_2.pdf},
}

@Article{floeck_etal_2010,
  author          = {Floeck, F. and Putzke, J. and Steinfels, S. and Fischbach, K. and Schoder, D.},
  title           = {Imitation and quality of tags in social bookmarking systems - Collective intelligence leading to folksonomies},
  journal         = {Advances in Intelligent and Soft Computing},
  year            = {2010},
  volume          = {76},
  pages           = {75-91},
  note            = {cited By},
  abstract        = {Social bookmarking platforms often allow users to see a list of tags that have been used previously for the webpage they are currently bookmarking, and from which they can select. In this paper, the authors analyze the influences of this feature on the tag categorizations resulting from the collaborative tagging effort. The main research goal is to show how the interface design of social bookmarking systems can influence the quality of the collective output of their users. Findings from a joint research project with the largest Russian social bookmarking site BobrDobr.ru suggest that if social bookmarking systems allow users to view the most popular tags, the overall variation of keywords used that are assigned to websites by all users decreases. © 2010 Springer-Verlag Berlin Heidelberg.},
  affiliation     = {University of Cologne, Department of Information Systems and Information Management, Pohligstr. 1, 50969 Cologne (Köln), Germany},
  author_keywords = {Collaborative Tagging; Collective Intelligence; Folksonomies; Shared Knowledge; Social-Bookmarking-Systems},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-14481-3_7},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-642-14481-3_7},
}

@Conference{darvish_chin_2010,
  author          = {Darvish, S. and Chin, A.},
  title           = {Dealing with the video tidal wave: The relevance of expertise for video tagging},
  year            = {2010},
  pages           = {289-290},
  note            = {cited By},
  abstract        = {The vast amounts of video that need to be tagged preclude the exclusive use of professional indexers. Thus a significant amount of video will need to be tagged by non-experts. Are the tags created by experts demonstrably superior to those of non-experts, and when non-experts have to be used for tagging, is it better to rely on tags created by those who upload videos or on others who watch the videos? Two related studies were carried out, the first where domain experts and others tagged videos, and the second where experts and others rated the relevance of tags that had been assigned to videos in the first study. Expert tags were judged to be more relevant by both experts and non-experts, with non-expert viewers also creating significantly better tags than did owners (i.e., the people who uploaded the videos). While significant differences were observed, the mean overall judged relevance of tags was relatively low, even for experts. Thus there seems to be considerable scope for the use of tag recommendation systems and other tools that can make the tagging process more consistent.},
  affiliation     = {Research and Innovation, George Brown College, 160 Kendal Avenue, Toronto, ON M5T 2T9, Canada; Nokia Research Center, Building 2, No. 5 Donghuan Zhonglu, Beijing, 100176, China},
  author_keywords = {Categorization; Classification; Collaborative tagging; Tagging systems; Video annotation},
  document_type   = {Conference Paper},
  doi             = {10.1145/1810617.1810679},
  journal         = {HT'10 - Proceedings of the 21st ACM Conference on Hypertext and Hypermedia},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-77954921226&doi=10.1145%2f1810617.1810679&partnerID=40&md5=51a6ff324fcb52ebbcc8597af6bedb0b},
}

@InProceedings{fenza_etal_2011,
  author    = {G. Fenza and V. Loia and S. Senatore},
  title     = {Agent-assisted tagging aimed at folkonomy-based information retrieval},
  booktitle = {2011 IEEE Symposium on Intelligent Agent (IA)},
  year      = {2011},
  pages     = {1-8},
  month     = {April},
  abstract  = {The wide diffusion of community tagging sites and related folksonomies has made the knowledge discovery and retrieval still much more urgent topic. If tagging systems allow users to add freely keywords to web resources, clicking on a tag has the side effect of a tag-based query, since enables the users to explore related content. The collective knowledge expressed though user-annotated data has a big potential, but needs to be filtered in a digest form so that the search result better reflects the users' preferences and actual aims. Starting from these considerations, our work presents an agent-based approach for a scalable semi-automatic generation of annotation tags, personalized on each user's preferences and tastes. Primary is the role of agents which assist users in the tagging activities as well as the retrieval of resources related to their interest. A user-friendly interface proposes an integrated one-shot view for interacting with a tagging system.},
  doi       = {10.1109/IA.2011.5953613},
  keywords  = {Internet;Web sites;data mining;human computer interaction;multi-agent systems;query processing;user interfaces;Web resources;agent-assisted tagging system;annotation tag semi-automatic generation;community tagging sites;folkonomy-based information retrieval;knowledge discovery;knowledge retrieval;tag-based query;user-annotated data;user-friendly interface;Communities;Computational modeling;Context;Lattices;Tagging;Web pages;Fuzzy Formal Concept Analysis (FFCA);Multi-agent system;Personalization;Tagging},
}

@Article{lu_etal_2011,
  author   = {C. Lu and X. Hu and J. r. Park},
  title    = {Exploiting the Social Tagging Network for Web Clustering},
  journal  = {IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans},
  year     = {2011},
  volume   = {41},
  number   = {5},
  pages    = {840-852},
  month    = {Sept},
  issn     = {1083-4427},
  abstract = {Social tagging is a major characteristic of Web 2.0. A social tagging system can be modeled with a tripartite network of users, resources, and tags. In this paper, we investigate how to enhance Web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method called “Tripartite Clustering” which clusters the three types of nodes (resources, users, and tags) simultaneously by only utilizing the links in the social tagging network. We also investigate two other approaches to exploit social tagging for clustering with K-means and Link K-means. All the clustering methods are experimented on a real-world social tagging data set sampled from del.icio.us. The clustering results are evaluated against a human-maintained Web directory. The experimental results show that the social tagging network is a very useful information source for document clustering. All social-annotation-based clustering methods can significantly improve the performance of content-based clustering. Compared to social-annotation-based K-means and Link K-means, Tripartite Clustering achieves equivalent or better performance and produces more useful information.},
  doi      = {10.1109/TSMCA.2011.2157128},
  keywords = {Internet;document handling;information resources;pattern clustering;Link K-means;Web 2.0;Web clustering;content based clustering;document clustering;human maintained Web directory;information source;social annotation based K-means;social annotation based clustering method;social tagging data set;social tagging network;tripartite clustering;tripartite network;Clustering algorithms;Clustering methods;Measurement;Semantics;Tagging;Vectors;Web pages;Clustering methods;social annotation;social tagging;tripartite network},
}

@InProceedings{yang_toderici_2011,
  author    = {W. Yang and G. Toderici},
  title     = {Discriminative tag learning on YouTube videos with latent sub-tags},
  booktitle = {CVPR 2011},
  year      = {2011},
  pages     = {3217-3224},
  month     = {June},
  abstract  = {We consider the problem of content-based automated tag learning. In particular, we address semantic variations (sub-tags) of the tag. Each video in the training set is assumed to be associated with a sub-tag label, and we treat this sub-tag label as latent information. A latent learning framework based on LogitBoost is proposed, which jointly considers both the tag label and the latent sub-tag label. The latent sub-tag information is exploited in our framework to assist the learning of our end goal, i.e., tag prediction. We use the cowatch information to initialize the learning process. In experiments, we show that the proposed method achieves significantly better results over baselines on a large-scale testing video set which contains about 50 million YouTube videos.},
  doi       = {10.1109/CVPR.2011.5995402},
  issn      = {1063-6919},
  keywords  = {content-based retrieval;learning (artificial intelligence);social networking (online);video retrieval;LogitBoost;YouTube videos;content-based automated tag learning;discriminative tag learning;latent learning framework;latent sub-tag label;tag label;Feature extraction;Semantics;Support vector machines;Tagging;Training;Videos;YouTube},
}

@Conference{das_etal_2011,
  author          = {Das, M. and Das, G. and Hristidis, V.},
  title           = {Leveraging collaborative tagging for web item design},
  year            = {2011},
  pages           = {538-546},
  note            = {cited By},
  abstract        = {The popularity of collaborative tagging sites has created new challenges and opportunities for designers of web items, such as electronics products, travel itineraries, popular blogs, etc. An increasing number of people are turning to online reviews and user-specified tags to choose from among competing items. This creates an opportunity for designers to build items that are likely to attract desirable tags when published. In this paper, we consider a novel optimization problem: given a training dataset of existing items with their user-submitted tags, and a query set of desirable tags, design the k best new items expected to attract the maximum number of desirable tags. We show that this problem is NP-Complete, even if simple Naive Bayes Classifiers are used for tag prediction. We present two principled algorithms for solving this problem: (a) an exact "two-tier" algorithm (based on top-k querying techniques), which performs much better than the naive brute-force algorithm and works well for moderate problem instances, and (b) a novel polynomialtime approximation algorithm with provable error bound for larger problem instances. We conduct detailed experiments on synthetic and real data crawled from the web to evaluate the efficiency and quality of our proposed algorithms. Copyright 2011 ACM.},
  affiliation     = {Department of Computer Science and Engineering, University of Texas, Arlington, United States; School of Computing and Information Sciences, Florida International University, United States},
  author_keywords = {Collaborative tagging; Item design; Naive bayes; Optimization},
  document_type   = {Conference Paper},
  doi             = {10.1145/2020408.2020493},
  journal         = {Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052690914&doi=10.1145%2f2020408.2020493&partnerID=40&md5=a195c6db2a96937120ae37fe6287e17a},
}

@Article{kichou_etal_2011,
  author          = {Kichou, S. and Mellah, H. and Amghar, Y. and Dahak, F.},
  title           = {Tags weighting based on user profile},
  journal         = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year            = {2011},
  volume          = {6890 LNCS},
  pages           = {206-216},
  note            = {cited By},
  abstract        = {The 'Collaborative Tagging' is gaining popularity on Web 2.0, this new generation of Web which makes user reader/writer. The 'Tagging' is a mean for users to express themselves freely through additions of label called 'Tags' to shared resources. One of the problems encountered in current tagging systems is to define the most appropriate tag for a resource. Tags are typically listed in order of popularity, as del-icio-us. But the popularity of the tag does not always reflect its importance and representativeness for the resource to which it is associated. Starting from the assumptions that the same tag for a resource can take different meanings for different users, and a tag from a knowledgeable user would be more important than a tag from a novice user, we propose an approach for weighting resource's tags based on user profile. For this we define a user model for his integration in the tag weight calculation and a formula for this calculation, based on three factors namely the user, the degree of approximation between his interest centers and the resource field, expertise and personal assessment for tags associated to the resource. A resource descriptor containing the best tags is created. © 2011 Springer-Verlag.},
  affiliation     = {Research Center on Scientific and Technical Information (CERIST) Benaknoun, Algiers, Algeria; INSA of Lyon, Computing on Images and Information Systems Laboratory, Lyon, France; National Hight School of Computer Science, ESI, Oued Smar, Algiers, Algeria},
  author_keywords = {Collaborative Tagging; Information retrieval; User profile},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-23620-4_24},
  source          = {Scopus},
  url             = {https://link.springer.com/chapter/10.1007%2F978-3-642-23620-4_24},
}

@Conference{gohr_etal_2011,
  author          = {Gohr, A. and Hinneburg, A. and Spiliopoulou, M. and Usbeck, R.},
  title           = {On the distinctiveness of tags in collaborative tagging systems},
  year            = {2011},
  note            = {cited By},
  abstract        = {We study in a quantitative way whether the most popular tags in a collaborative tagging system are distinctive features when looking at the underlying content. For any set of annotations being helpful in searching, this property must necessarily hold to a strong degree. Our initial experiments show that the most frequent tags in CiteULike are distinctive features, despite the process of annotating documents is not centrally coordinated nor correction mechanisms like in a Wiki-system are used. © 2011 ACM.},
  affiliation     = {Computer Science, Martin-Luther-Univerisity, Halle, Germany; Computer Science, Otto-von-Guericke-University, Magdeburg, Germany},
  author_keywords = {collaborative information systems; tagging; text mining},
  document_type   = {Conference Paper},
  doi             = {10.1145/1988688.1988760},
  journal         = {ACM International Conference Proceeding Series},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960605362&doi=10.1145%2f1988688.1988760&partnerID=40&md5=ef7d11f6fa78d00949aa1d7500b8eabd},
}

@InProceedings{koh_etal_2012,
  author    = {J. L. Koh and N. Shongwe and C. W. Cho},
  title     = {A multi-level hierarchical index structure for supporting efficient similarity search on tag sets},
  booktitle = {2012 Sixth International Conference on Research Challenges in Information Science (RCIS)},
  year      = {2012},
  pages     = {1-12},
  month     = {May},
  abstract  = {Social communication websites has been an emerging type of a Web service that helps users to share their resources. For providing efficient similarity search of tag set in a social tagging system, we propose a multi-level hierarchical index structure to group similar tag sets. Not only the algorithms of similarity searches of tag sets, but also the algorithms of deletion and updating of tag sets by using the constructed index structure are provided. Furthermore, we define a modified hamming distance function on tag sets, which consider the semantically relatedness when comparing the members for evaluating the similarity of two tag sets. This function is more applicable to evaluate the similarity search of two tag sets. A systematic performance study is performed to verify the effectiveness and the efficiency of the proposed strategies. The experiment results show that the proposed MHIB approach further improves the pruning effect of the previous work which constructs a two-level index structure. Especially, the MHIB approach is well scalable with respect to the three parameters when using either the hamming distance or the modified hamming distance for similarity measure. Although the insertion operation of the MHIB approach requires higher cost than the naïve method, with the assistant of the constructed inverted list of clusters, it performs faster than the previous work. Besides, the cost of performing deletion operation by using the MHIB approach is much less than the other two approaches and so is the update operation.},
  doi       = {10.1109/RCIS.2012.6240436},
  issn      = {2151-1349},
  keywords  = {Web services;information retrieval;resource allocation;social networking (online);MHIB approach;Web service;efficient similarity search;modified hamming distance function;multilevel hierarchical index structure;resource sharing;similar tag set grouping;social communication Web sites;social tagging system;tag sets deletion;two-level index structure;Hamming distance;Indexing;Search problems;Tagging;Transaction databases;Upper bound;Social tagging;index structure;similarity search},
}

@InProceedings{zeng_etal_2013,
  author    = {G. Zeng and P. Luo and E. Chen and M. Wang},
  title     = {From Social User Activities to People Affiliation},
  booktitle = {2013 IEEE 13th International Conference on Data Mining},
  year      = {2013},
  pages     = {1277-1282},
  month     = {Dec},
  abstract  = {This study addresses the problem of inferring users' employment affiliation information from social activities. It is motivated by the applications which need to monitoring and analyzing the social activities of the employees from a given company, especially their social tracks related to the work and business. It definitely helps to better understand their needs and opinions towards certain business area, so that the account sales targeting these customers in the given company can adjust the sales strategies accordingly. Specifically, in this task we are given a snapshot of a social network and some labeled social users who are the employees of a given company. Our goal is to identify more users from the same company. We formulate this problem as a task of classifying nodes over a graph, and develop a Supervised Label Propagation model. It naturally incorporates the rich set of features for social activities, models the networking effect by label propagation, and learns the feature weights so that the labels are propagated to the right users. To validate its effectiveness, we show our case studies on identifying the employees of "China Telecom" and "China Unicom" from Sina Weibo. The experimental results show that our method significantly outperforms the compared baseline ones.},
  doi       = {10.1109/ICDM.2013.101},
  issn      = {1550-4786},
  keywords  = {learning (artificial intelligence);social networking (online);China Telecom;China Unicom;social network;social user activities;supervised label propagation model;Companies;Equations;Media;Social network services;Telecommunications;Vectors},
}

@InProceedings{saari_etal_2013,
  author    = {P. Saari and M. Barthet and G. Fazekas and T. Eerola and M. Sandler},
  title     = {Semantic models of musical mood: Comparison between crowd-sourced and curated editorial tags},
  booktitle = {2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)},
  year      = {2013},
  pages     = {1-6},
  month     = {July},
  abstract  = {Social media services such as Last.fm provide crowd-sourced mood tags which are a rich but often noisy source of information. In contrast, editorial annotations from production music libraries are meant to be incisive in nature. We compare the efficiency of these two data sources in capturing semantic information on mood expressed by music. First, a semantic computing technique devised for mood-related tags in large datasets is applied to Last.fm and I Like Music (ILM) corpora separately (250,000 tracks each). The resulting semantic estimates are then correlated with listener ratings of arousal, valence and tension. High correlations (Spearman's rho) are found between the track positions in the dimensional mood spaces and listener ratings using both data sources (0.60 <; rs <; 0.70). In addition, the use of curated editorial data provides a statistically significant improvement compared to crowd-sourced data for predicting moods perceived in music.},
  doi       = {10.1109/ICMEW.2013.6618436},
  keywords  = {behavioural sciences;correlation methods;music;semantic Web;social networking (online);I Like Music corpora;ILM corpora;Last.fm corpora;Spearman rho;crowd-sourced data;crowd-sourced mood tags;curated editorial tags;data sources;editorial annotations;listener ratings;mood-related tags;musical mood;noisy information source;production music libraries;semantic computing technique;semantic information;semantic models;social media services;Correlation;Editorials;Mood;Production;Semantics;Vectors;Vocabulary;Semantic computing;affective circumplex transformation;dimensional emotion model;music moods},
}

@InProceedings{chen_etal_2013,
  author    = {S. Chen and S. Owusu and L. Zhou},
  title     = {Social Network Based Recommendation Systems: A Short Survey},
  booktitle = {2013 International Conference on Social Computing},
  year      = {2013},
  pages     = {882-885},
  month     = {Sept},
  abstract  = {This paper examines the background of the recommender systems and the state-of-art technologies in this current research area. We examined the needs for recommendation systems and the enhanced performance by using the additional social network information. We compared different types of social network information and how they are used to improve the recommendation systems with a few examples. We also discussed different evaluation methods that are important to measure the performances of a recommendation system. We concluded that the recommendation system is a very important research area and could be applied in many domains. We summarized different measures towards evaluating a recommendation system especially those incorporating the social network information.},
  doi       = {10.1109/SocialCom.2013.134},
  keywords  = {recommender systems;social networking (online);social network based recommendation systems;social network information;state-of-art technologies;Accuracy;Bayes methods;Collaboration;Measurement;Prediction algorithms;Recommender systems;Social network services;Algorithms;Artificial Intelligence;Recommendation Systems},
}

@Article{figueiredo_etal_2013,
  author   = {Flavio Figueiredo and Henrique Pinto and Fabiano Belém and Jussara Almeida and Marcos Gonçalves and David Fernandes and Edleno Moura},
  title    = {Assessing the quality of textual features in social media},
  journal  = {Information Processing \& Management},
  year     = {2013},
  volume   = {49},
  number   = {1},
  pages    = {222 - 247},
  issn     = {0306-4573},
  doi      = {http://dx.doi.org/10.1016/j.ipm.2012.03.003},
  keywords = {Web 2.0, Textual features, Information quality, Social media},
  url      = {http://www.sciencedirect.com/science/article/pii/S0306457312000465},
}

@InProceedings{demir_oezguer_2014,
  author    = {H. Demir and A. Özgür},
  title     = {Improving Named Entity Recognition for Morphologically Rich Languages Using Word Embeddings},
  booktitle = {2014 13th International Conference on Machine Learning and Applications},
  year      = {2014},
  pages     = {117-122},
  month     = {Dec},
  abstract  = {In this paper, we addressed the Named Entity Recognition (NER) problem for morphologically rich languages by employing a semi-supervised learning approach based on neural networks. We adopted a fast unsupervised method for learning continuous vector representations of words, and used these representations along with language independent features to develop a NER system. We evaluated our system for the highly inflectional Turkish and Czech languages. We improved the state-of-the-art F-score obtained for Turkish without using gazetteers by 2.26% and for Czech by 1.53%. Unlike the previous state-of-the-art systems developed for these languages, our system does not make use of any language dependent features. Therefore, we believe it can easily be applied to other morphologically rich languages.},
  doi       = {10.1109/ICMLA.2014.24},
  keywords  = {natural language processing;neural nets;unsupervised learning;word processing;Czech languages;F-score;NER problem;NER system;Turkish languages;continuous word vector representations;language independent features;morphologically rich languages;named entity recognition problem;neural networks;semisupervised learning approach;unsupervised learning method;word embeddings;Cities and towns;Context;Measurement;Neural networks;Semisupervised learning;Training;Vectors;Czech NER;Named Entity Recognition;Skip-gram;Turkish NER;Word Embeddings, rank1},
}

@InProceedings{shan_etal_2015,
  author    = {Lili Shan and Chengjie Sun and Lei Lin and Ming Liu and Xiaolong Wang and Bingquan Liu},
  title     = {Evaluating tag quality for blogger modelling via topic models},
  booktitle = {2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)},
  year      = {2015},
  pages     = {1770-1776},
  month     = {Aug},
  abstract  = {With the permission of annotating blog posts with tags, tags has become one of the most important resources used to describe blogger features. However, due to the irregular quality of tags, not all tags are appropriate for representing blogger's preferences. Poor tags or spam tags confuse the actual user's preferences and spam terms, thus they should be detected before they are directly used to tag bloggers. A detailed quantitative analysis on the categories of tag spam in the blogosphere is presented in this paper. Taking advantage of abundant text contents in blog posts and the relatively stable semantic relationship between tags and their target posts, an unsupervised approach based on topic models is proposed to evaluate tag quality for blogger modelling in the blogosphere. The latent interest topics of a blogger are mined out through Latent Dirichlet Allocation (LDA) topic modeling. The blog post of the blogger is represented as a distribution over latent topics and a latent topic is a distribution over words of the vocabulary. A tag is also expressed as a specific co-occurrence term vector. Ultimately, a scheme is devised to determine the similarity between each tag and its target blog post. Then the tags with less similarity value can be identified as poor tag. The experimental results indicate that the proposed method achieves more promising performance than the baselines on datasets collected from Sina Blog, which is one of the biggest Chinese blogs.},
  doi       = {10.1109/FSKD.2015.7382215},
  keywords  = {social networking (online);text analysis;Chinese blogs;LDA topic modeling;Sina Blog;blog post annotation;blog posts;blogger features;blogger modelling;blogger preference representation;co-occurrence term vector;irregular tag quality;latent Dirichlet allocation topic modeling;latent interest topic mining;quantitative analysis;relatively stable semantic relationship;similarity value;tag quality evaluation;tag spam;text contents;topic models;unsupervised approach;user preferences;vocabulary words;Adaptation models;Blogs;Collaboration;Reliability;Resource management;Semantics;Tagging;blog representation;semantic similarity;tag quality evaluation;topic model},
}

@Article{caeiro-rodriguez_etal_2015,
  author   = {M. Caeiro-Rodríguez and J. M. Santos-Gago and M. Lama and M. Llamas-Nistal},
  title    = {A Keyword Recommendation Experiment to Support Information Organization and Folksonomies in Edu-AREA},
  journal  = {IEEE Revista Iberoamericana de Tecnologias del Aprendizaje},
  year     = {2015},
  volume   = {10},
  number   = {2},
  pages    = {60-68},
  month    = {May},
  issn     = {1932-8540},
  abstract = {Edu-AREA is a Web 2.0 application whose main goal is to contribute to teaching innovation. It provides descriptions of educational resources and guidelines that can be used by teachers to create their lesson plans and later to document their teaching experiences. At the current stage of the Edu-AREA development, a main issue is related to the management and classification of information provided by users. This paper introduces a folksonomy approach, the architecture of the system, and the results of an experiment about keyword recommendations.},
  doi      = {10.1109/RITA.2015.2417953},
  keywords = {pattern classification;recommender systems;social networking (online);teaching;Edu-AREA;Web 2.0 application;educational guidelines;educational resources;folksonomy approach;information classification;information management;information organization;keyword recommendation;lesson plan creation;system architecture;teaching experience documentation;teaching innovation;Data models;Education;Ontologies;Organizations;Semantics;Vocabulary;Web 2.0;Educational activities;Information sharing;Social network services;Tagging;educational activities;information sharing;tagging},
}

@Article{lu_etal_2015,
  author   = {Jie Lu and Dianshuang Wu and Mingsong Mao and Wei Wang and Guangquan Zhang},
  title    = {Recommender system application developments: A survey},
  journal  = {Decision Support Systems},
  year     = {2015},
  volume   = {74},
  pages    = {12 - 32},
  issn     = {0167-9236},
  doi      = {http://dx.doi.org/10.1016/j.dss.2015.03.008},
  keywords = {Recommender systems, E-service personalization, E-commerce, E-learning, E-government},
  url      = {http://www.sciencedirect.com/science/article/pii/S0167923615000627},
}

@Article{zou_etal_2015,
  author   = {Benyou Zou and Cuiping Li and Liwen Tan and Hong Chen},
  title    = {GPUTENSOR: Efficient tensor factorization for context-aware recommendations},
  journal  = {Information Sciences},
  year     = {2015},
  volume   = {299},
  pages    = {159 - 177},
  issn     = {0020-0255},
  abstract = {Recommendation systems play an important role in many practical applications that help users manage information and provide personalized recommendations. The context in which a choice is made is an important factor for recommendation systems. Recently, researchers extended the classical matrix factorization to enable generic integration of contextual information by modeling the relevant data as a tensor. However, current tensor factorization methods have two important limitations: (1) computing costs can be very high in practice and (2) they can only be applied to static conditions. In this paper, we propose the GPUTENSOR, a parallel tensor factorization algorithm, to accelerate the tensor factorization of large datasets to support efficient context-aware recommendations. The basic idea of this algorithm is to partition a tensor into smaller blocks and then exploit the inherent parallelism and high memory bandwidth of Graphics Processing Units (GPU) to perform tensor related operations in parallel. Furthermore, based on the observation that contextual information changes dynamically and frequently, we present GPUTENSOR+, an effective incremental method that models the apparent changes of a tensor by adaptively updating its previously factorized components instead of recomputing them on the whole dataset every time data are changed. Experiments indicate that the proposed methods can achieve significant timesavings without a significant loss in accuracy. As an important data mining tool, these methods can be used a potential basis for many other interesting tensor applications, such as clustering, trend detection, social network analysis, and latent concept discovery.},
  doi      = {http://dx.doi.org/10.1016/j.ins.2014.12.004},
  keywords = {Recommendation system, Context-aware, Tensor factorization},
  url      = {http://www.sciencedirect.com/science/article/pii/S0020025514011414},
}

@Article{klasnja-milicevic_etal_2015,
  author          = {Klašnja-Milićević, A. and Ivanović, M. and Nanopoulos, A.},
  title           = {Recommender systems in e-learning environments: a survey of the state-of-the-art and possible extensions},
  journal         = {Artificial Intelligence Review},
  year            = {2015},
  volume          = {44},
  number          = {4},
  pages           = {571-604},
  note            = {cited By},
  abstract        = {With the development of sophisticated e-learning environments, personalization is becoming an important feature in e-learning systems due to the differences in background, goals, capabilities and personalities of the large numbers of learners. Personalization can achieve using different type of recommendation techniques. This paper presents an overview of the most important requirements and challenges for designing a recommender system in e-learning environments. The aim of this paper is to present the various limitations of the current generation of recommendation techniques and possible extensions with model for tagging activities and tag-based recommender systems, which can apply to e-learning environments in order to provide better recommendation capabilities. © 2015, Springer Science+Business Media Dordrecht.},
  affiliation     = {University of Novi Sad, Novi Sad, Serbia; Katholische Universität Eichstätt-Ingolstadt, Ostenstraße 26, Eichstätt, Germany},
  author_keywords = {Collaborative tagging; e-Learning; Personalization; Recommender systems},
  document_type   = {Article},
  doi             = {10.1007/s10462-015-9440-z},
  source          = {Scopus},
  url             = {https://link.springer.com/article/10.1007%2Fs10462-015-9440-z},
}

@PhdThesis{sharif_2009,
  author = {Sharif, Atefeh},
  title  = {Combining ontology and folksonomy: An Integrated Approach to Knowledge Representation},
  year   = {2009},
  url    = {https://core.ac.uk/download/pdf/11887239.pdf},
}

@Article{bouadjenek_etal_2016,
  author   = {Mohamed Reda Bouadjenek and Hakim Hacid and Mokrane Bouzeghoub},
  title    = {Social networks and information retrieval, how are they converging? A survey, a taxonomy and an analysis of social information retrieval approaches and platforms},
  journal  = {Information Systems},
  year     = {2016},
  volume   = {56},
  pages    = {1 - 18},
  issn     = {0306-4379},
  doi      = {http://dx.doi.org/10.1016/j.is.2015.07.008},
  keywords = {Information Retrieval, Social networks, Social Information Retrieval, Social search, Social recommendation, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S030643791500160X},
}

@Article{spyrou_mylonas_2016,
  author   = {Evaggelos Spyrou and Phivos Mylonas},
  title    = {A survey on Flickr multimedia research challenges},
  journal  = {Engineering Applications of Artificial Intelligence},
  year     = {2016},
  volume   = {51},
  pages    = {71 - 91},
  issn     = {0952-1976},
  note     = {Mining the Humanities: Technologies and Applications},
  doi      = {http://dx.doi.org/10.1016/j.engappai.2016.01.006},
  keywords = {Flickr, Retrieval, Recommendations, Localization, Travel applications, Knowledge extraction, rank1},
  url      = {http://www.sciencedirect.com/science/article/pii/S0952197616000105},
}

@Article{tibely_etal_2016,
  author        = {Tibély, G. and Pollner, P. and Palla, G.},
  title         = {Comparing the hierarchy of author given tags and repository given tags in a large document archive},
  journal       = {European Physical Journal: Special Topics},
  year          = {2016},
  volume        = {225},
  number        = {10},
  pages         = {2025-2032},
  note          = {cited By},
  abstract      = {Folksonomies – large databases arising from collaborative tagging of items by independent users - are becoming an increasingly important way of categorizing information. In these systems users can tag items with free words, resulting in a tripartite item-tag-user network. Although there are no prescribed relations between tags, the way users think about the different categories presumably has some built in hierarchy, in which more special concepts are descendants of some more general categories. Several applications would benefit from the knowledge of this hierarchy. Here we apply a recent method to check the differences and similarities of hierarchies resulting from tags given by independent individuals and from tags given by a centrally managed repository system. The results from our method showed substantial differences between the lower part of the hierarchies, and in contrast, a relatively high similarity at the top of the hierarchies. © 2016, EDP Sciences and Springer.},
  affiliation   = {Dept. of Biological Physics, Eötvös University, Budapest, Hungary; MTA-ELTE Statistical and Biological Physics Research Group, Hungarian Academy of Sciences, Budapest, Hungary},
  document_type = {Article},
  doi           = {10.1140/epjst/e2015-50154-9},
  source        = {Scopus},
  url           = {https://link.springer.com/article/10.1140%2Fepjst%2Fe2015-50154-9},
}

@Article{godoy_corbellini_2016,
  author        = {Godoy, D. and Corbellini, A.},
  title         = {Folksonomy-Based Recommender Systems: A State-of-the-Art Review},
  journal       = {International Journal of Intelligent Systems},
  year          = {2016},
  volume        = {31},
  number        = {4},
  pages         = {314-346},
  note          = {cited By},
  abstract      = {Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines. © 2015 Wiley Periodicals, Inc.},
  affiliation   = {ISISTAN Research Institute, Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Universidad Nacional Del Centro de la Provincia de Buenos Aires (UNICEN), University Campus, Paraje Arroyo Seco (BBO7001B), Tandil, Buenos Aires, Argentina},
  document_type = {Review},
  doi           = {10.1002/int.21753},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84957435503&doi=10.1002%2fint.21753&partnerID=40&md5=d5cdd0d6da89b95cdfd87fb1b249a531},
}

@Article{sun_etal_2016,
  author        = {Sun, Y. and Sun, H. and Cheng, R.},
  title         = {Fast and semantic measurements on collaborative tagging quality},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2016},
  volume        = {9652 LNAI},
  pages         = {363-375},
  note          = {cited By},
  abstract      = {This paper focuses on the problem of tagging quality evaluation in collaborative tagging systems. By investigating the dynamics of tagging process, we find that high frequency tags almost cover the main aspects of a resource content and can be determined stable much earlier than a whole tag set. Motivated by this finding, we design the swapping index and smart moving index on tagging quality. We also study the correlations in tag usage and propose the semantic measurement on tagging quality. The proposed methods are evaluated against real datasets and the results show that they are more efficient than previous methods, which are appropriate for a large number of web resources. The effectiveness is justified by the results in tag based applications. The light weight metrics bring a little loss on the performance, while the semantic metric is better than current methods. © Springer International Publishing Switzerland 2016.},
  affiliation   = {School of Computer Science and Technology, Shandong University, Jinan, China; Engineering Research Center of Digital Media Technology, MOE, Jinan, China; Software College, Shandong University, Jinan, China; Department of Computer Science, The University of Hong Kong, Hong Kong},
  document_type = {Conference Paper},
  doi           = {10.1007/978-3-319-31750-2_29},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007%2F978-3-319-31750-2_29},
}

@Article{yang_wang_2017,
  author   = {J. Yang and J. Wang},
  title    = {Tag clustering algorithm LMMSK: Improved K-means algorithm based on latent semantic analysis},
  journal  = {Journal of Systems Engineering and Electronics},
  year     = {2017},
  volume   = {28},
  number   = {2},
  pages    = {374-384},
  month    = {April},
  abstract = {With the wide application of Web_2.0 and social software, there are more and more tag-related studies and applications. Because of the randomness and the personalization in users' tagging, tag research continues to encounter data space and semantics obstacles. With the min-max similarity (MMS) to establish the initial centroids, the traditional K-means clustering algorithm is firstly improved to the MMSK-means clustering algorithm, the superiority of which has been tested; based on MMSK-means and combined with latent semantic analysis (LSA), here secondly emerges a new tag clustering algorithm, LMMSK. Finally, three algorithms for tag clustering, MMSK-means, tag clustering based on LSA (LSA-based algorithm) and LMMSK, have been run on Matlab, using a real tag-resource dataset obtained from the Delicious Social Bookmarking System from 2004 to 2009. LMMSK's clustering result turns out to be the most effective and the most accurate. Thus, a better tag-clustering algorithm is found for greater application of social tags in personalized search, topic identification or knowledge community discovery. In addition, for a better comparison of the clustering results, the clustering corresponding results matrix (CCR matrix) is proposed, which is promisingly expected to be an effective tool to capture the evolutions of the social tagging system.},
  doi      = {10.21629/JSEE.2017.02.18},
  keywords = {Algorithm design and analysis;Clustering algorithms;Clustering methods;Data processing;Semantics;Tagging;Web 2.0;K-means;latent semantic analysis (LSA);min-max similarity (MMS);social tagging;tag clustering algorithm},
}

@InProceedings{yang_etal_2017a,
  author    = {L. C. Yang and S. Y. Chou and J. Y. Liu and Y. H. Yang and Y. A. Chen},
  title     = {Revisiting the problem of audio-based hit song prediction using convolutional neural networks},
  booktitle = {2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year      = {2017},
  pages     = {621-625},
  month     = {March},
  abstract  = {Being able to predict whether a song can be a hit has important applications in the music industry. Although it is true that the popularity of a song can be greatly affected by external factors such as social and commercial influences, to which degree audio features computed from musical signals (whom we regard as internal factors) can predict song popularity is an interesting research question on its own. Motivated by the recent success of deep learning techniques, we attempt to extend previous work on hit song prediction by jointly learning the audio features and prediction models using deep learning. Specifically, we experiment with a convolutional neural network model that takes the primitive mel-spectrogram as the input for feature learning, a more advanced JYnet model that uses an external song dataset for supervised pre-training and auto-tagging, and the combination of these two models. We also consider the inception model to characterize audio information in different scales. Our experiments suggest that deep structures are indeed more accurate than shallow structures in predicting the popularity of either Chinese or Western Pop songs in Taiwan. We also use the tags predicted by JYnet to gain insights into the result of different models.},
  doi       = {10.1109/ICASSP.2017.7952230},
  keywords  = {audio signal processing;learning (artificial intelligence);music;neural nets;Chinese Pop songs;JYnet model;Western Pop songs;audio features;audio information;audio-based hit song prediction;autotagging;convolutional neural network model;deep learning;deep structures;feature learning;inception model;music industry;prediction models;primitive mel-spectrogram;song dataset;supervised pretraining;Computational modeling;Convolution;Machine learning;Music;Neural networks;Predictive models;Training;Hit song prediction;convolutional neural network;cultural factors;deep learning;music tags},
}

@InBook{klasnja-milicevic_etal_2017,
  pages     = {77--112},
  title     = {Folksonomy and Tag-Based Recommender Systems in E-Learning Environments},
  publisher = {Springer International Publishing},
  year      = {2017},
  author    = {Kla{\v{s}}nja-Mili{\'{c}}evi{\'{c}}, Aleksandra and Vesin, Boban and Ivanovi{\'{c}}, Mirjana and Budimac, Zoran and Jain, Lakhmi C.},
  address   = {Cham},
  isbn      = {978-3-319-41163-7},
  abstract  = {Collaborative tagging is technique, highly employed in different domains, which is used for automatic analysis of users' preferences and recommendations. To improve recommendation quality, metadata such as content information of items has typically been used as additional knowledge. With the increasing reputation of the collaborative tagging systems, tags could be interesting and provide useful information to enhance algorithms for recommender systems. Besides helping user to organize his/her personal collections, a tag also can be regarded as a user's personal opinion expression, while tagging can be considered as implicit rating or voting on the tagged information resources or items. The overview, presented in this chapter includes descriptions of content-based recommender systems, collaborative filtering systems, hybrid approach, memory-based and model-based algorithms, features of collaborative tagging that are generally attributed to their success and popularity, as well as a model for tagging activities and tag-based recommender systems.},
  booktitle = {E-Learning Systems: Intelligent Techniques for Personalization},
  doi       = {10.1007/978-3-319-41163-7_7},
  url       = {https://doi.org/10.1007/978-3-319-41163-7_7},
}

@Article{kleinberg_1999,
  author     = {Kleinberg, Jon M.},
  title      = {Authoritative Sources in a Hyperlinked Environment},
  journal    = {J. ACM},
  year       = {1999},
  volume     = {46},
  number     = {5},
  pages      = {604--632},
  month      = sep,
  issn       = {0004-5411},
  acmid      = {324140},
  address    = {New York, NY, USA},
  doi        = {10.1145/324133.324140},
  issue_date = {Sept. 1999},
  keywords   = {World Wide Web, graph algorithms, hypertext structure, link analysis},
  numpages   = {29},
  publisher  = {ACM},
  url        = {http://doi.acm.org/10.1145/324133.324140},
}

@Article{kipp_campbell_2006,
  author        = {Kipp, M.E.I. and Campbell, D.G.},
  title         = {Patterns and inconsistencies in collaborative tagging systems: An examination of tagging practices},
  journal       = {Proceedings of the ASIST Annual Meeting},
  year          = {2006},
  volume        = {43},
  note          = {cited By},
  abstract      = {This paper analyzes the tagging patterns exhibited by users of del.icio.us, to assess how collaborative tagging supports and enhances traditional ways of classifying and indexing documents. Using frequency data and co-word analysis matrices analyzed by multi-dimensional scaling, the authors discovered that tagging practices to some extent work in ways that are continuous with conventional indexing. Small numbers of tags tend to emerge by unspoken consensus, and inconsistencies follow several predictable patterns that can easily be anticipated. However, the tags also indicated intriguing practices relating to time and task which suggest the presence of an extra dimension in classification and organization, a dimension which conventional systems are unable to facilitate.},
  affiliation   = {Faculty of Information and Media Studies, University of Western Ontario, Canada},
  document_type = {Conference Paper},
  keywords      = {rank1},
  page_count    = {9},
  source        = {Scopus},
  url           = {http://onlinelibrary.wiley.com/doi/10.1002/meet.14504301178/pdf},
}

@Article{guy_tonkin_2006,
  author  = {Guy, Marieke and Tonkin, Emma},
  title   = {Tidying up tags},
  journal = {D-lib Magazine},
  year    = {2006},
  volume  = {12},
  number  = {1},
  pages   = {1082--9873},
  url     = {http://www.dlib.org/dlib/january06/guy/01guy.html},
}

@Conference{al-khalifa_davis_2007,
  author          = {Al-Khalifa, H.S. and Davis, H.C.},
  title           = {Towards better understanding of folksonomic patterns},
  year            = {2007},
  pages           = {163-166},
  note            = {cited By},
  abstract        = {Folksonomies provide a free source of keywords describing web resources; however, these keywords are free form and their semantics spans multiple contextual dimension. In this paper, we present a pragmatic experiment that analyzes folksonomies using three classification categories: Personal, Factual and Subjective, in order to gain more understanding of the types of tags used in the social tagging process. The rational for this work was to measure the potential portion of folksonomy tags that might be helpful when considering the creation of structured metadata. Copyright 2007 ACM.},
  affiliation     = {Learning Societies Lab., ECS, Southampton University, SO17 1BJ, Southampton, United Kingdom},
  author_keywords = {Collaborative tagging; Folksonomy; Social bookmarking},
  document_type   = {Conference Paper},
  doi             = {10.1145/1286240.1286288},
  journal         = {Hypertext 2007: Proceedings of the Eighteenth ACM Conference on Hypertext and Hypermedia, HT'07},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1286240.1286288},
}

@Article{ji_etal_2007,
  author        = {Ji, A.-T. and Yeon, C. and Kim, H.-N. and Jo, G.-S.},
  title         = {Collaborative tagging in recommender systems},
  journal       = {Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)},
  year          = {2007},
  volume        = {4830 LNAI},
  pages         = {377-386},
  note          = {cited By},
  abstract      = {This paper proposes a collaborative filtering method with usercreated tags focusing on changes of web content and internet services. Collaborative tagging is employed as an approach in order to grasp and filter users' preferences for items. In addition, we explore several advantages of collaborative tagging for future searching and information sharing which is used for automatic analysis of user preference and recommendation. We present empirical experiments using real dataset from del.icio.us to demonstrate our algorithm and evaluate performance compared with existing works. © Springer-Verlag Berlin Heidelberg 2007.},
  affiliation   = {Intelligent E-Commerce Systems Laboratory, Inha University, South Korea},
  document_type = {Conference Paper},
  keywords      = {rank1},
  source        = {Scopus},
  url           = {https://link.springer.com/chapter/10.1007/978-3-540-76928-6_39},
}

@InProceedings{turnbull_etal_2008,
  author    = {Douglas Turnbull and Luke Barrington and Gert Lanckriet},
  title     = {Five approaches to collecting tags for music},
  booktitle = {In Proceedings of the 9th International Conference on Music Information Retrieval (ISMIR},
  year      = {2008},
  url       = {https://www.cs.swarthmore.edu/~turnbull/Papers/Turnbull_FiveTag_ISMIR08.pdf},
}

@Conference{chen_etal_2009,
  author          = {Chen, L. and Wright, P. and Nejdl, W.},
  title           = {Improving music genre classification using collaborative tagging data},
  year            = {2009},
  pages           = {84-93},
  note            = {cited By},
  abstract        = {As a fundamental and critical component of music information retrieval (MIR) systems, music genre classification has attracted considerable research attention. Automatically classifying music by genre is, however, a challenging problem due to the fact that music is an evolving art. While most of the existing work categorizes music using features extracted from music audio signals, in this paper, we propose to exploit the semantic information embedded in tags supplied by users of social networking websites. Particularly, we consider the tag information by creating a graph of tracks so that tracks are neighbors if they are similar in terms of their associated tags. Two classification methods based on the track graph are developed. The first one employs a classification scheme which simultaneously considers the audio content and neighborhood of tracks. In contrast, the second one is a two-level classifier which initializes genre label for unknown tracks using their audio content, and then iteratively updates the genres considering the influence from their neighbors. A set of optimizing strategies are designed for the purpose of further enhancing the quality of the two-level classifier. Extensive experiments are conducted on real-world data collected from Last.fm. Promising experimental results demonstrate the benefit of using tags for accurate music genre classification. Copyright 2009 ACM.},
  affiliation     = {L3S Research Center, Leibniz University, Hannover, Germany; College of Computing, Georgia Institute of Technology, United States},
  author_keywords = {Exploiting tag semantics; Music genre classification; Relaxation labelling},
  document_type   = {Conference Paper},
  doi             = {10.1145/1498759.1498812},
  journal         = {Proceedings of the 2nd ACM International Conference on Web Search and Data Mining, WSDM'09},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-70349129938&doi=10.1145%2f1498759.1498812&partnerID=40&md5=077ebcf735ba081473841ee54a0bad72},
}

@Article{memmel_etal_2009,
  author          = {Memmel, M. and Kockler, M. and Schirru, R.},
  title           = {Providing multi source tag recommendations in a social resource sharing platform},
  journal         = {Journal of Universal Computer Science},
  year            = {2009},
  volume          = {15},
  number          = {3},
  pages           = {678-691},
  note            = {cited By},
  abstract        = {In today's information environments, tagging is widely used to provide information about arbitrary types of digital resources. This information is usually created by end users with different motivations and for different kinds of purposes. When aiming to support users in the tagging process, these differences play an important role. In this paper several approaches to generate tag recommendations are discussed, and a prototypical recommender system for the social resource sharing platform ALOE is presented. This interactive system allows users to control the generation of the recommendations by selecting the sources to be used as well as their impact. The component was introduced at DFKI, and a first evaluation showed that the recommender component was considered as helpful by a majority of users. © J.UCS.},
  affiliation     = {Knowledge Management Department DFKI GmbH, Kaiserslautern, Germany},
  author_keywords = {Classification; Collaborative tagging; Digital resources; Knowledge management; Knowledge sharing; Metadata; Recommender; Tagging; Web 2.0},
  document_type   = {Article},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://www.jucs.org/jucs_15_3/providing_multi_source_tag/jucs_15_03_0678_0691_memmel.pdf},
}

@InProceedings{kungas_dumas_2009,
  author    = {P. Kungas and M. Dumas},
  title     = {Cost-Effective Semantic Annotation of XML Schemas and Web Service Interfaces},
  booktitle = {2009 IEEE International Conference on Services Computing},
  year      = {2009},
  pages     = {372-379},
  month     = {Sept},
  abstract  = {Research in the field of semantic Web services aims at automating the discovery, selection, composition and management of Web services based on semantic descriptions. However, the applicability of many solutions developed in this field is hampered by the costs associated with semantically annotating large repositories of Web services. To overcome this gap we propose a practical method for semantically annotating collections of XML Schemas and Web service interfaces. We have evaluated this method on a large repository of governmental Web services. The evaluation shows that relatively simple techniques are surprisingly cost-effective, saving hundreds of man-hours of semantic annotation effort. Moreover, the proposed method does not assume the availability of a preexisting ontology or controlled vocabulary. Instead, the space of annotations is dynamically built during the annotation process.},
  doi       = {10.1109/SCC.2009.17},
  keywords  = {Web services;XML;semantic Web;Web service interfaces;XML schemas;cost-effective semantic annotation;semantic Web services;Application software;Assembly;Humans;Middleware;Natural language processing;Natural languages;System testing;TV;Web services;XML;Web Services interfaces;semantic annotation},
}

@InProceedings{nakashima_etal_2009,
  author    = {M. Nakashima and T. Hiyama and K. Sato and T. Ito},
  title     = {Proceeding with Keyword-Based Web-Image Annotation Conceptually in Folksonomy},
  booktitle = {2009 International Conference on Complex, Intelligent and Software Intensive Systems},
  year      = {2009},
  pages     = {995-1000},
  month     = {March},
  abstract  = {Image annotation becomes increasingly more important as the Web continues to grow. We propose a novel approach to enhancing keyword-based Web-image annotation in folksonomy, where a volunteer user is notified what kind(s) of keywords are necessary, and that keywords have been sufficiently provided by other volunteer users. Necessary information is obtained from the keyword-filled facet tables, and sufficient information provided by the quantified conceptual keyword relations. Though the facet tables and the conceptual keyword relations are set by referring to an ontology, users do not need to know its details to successfully annotate the images.},
  doi       = {10.1109/CISIS.2009.115},
  keywords  = {Internet;image retrieval;ontologies (artificial intelligence);folksonomy system;image search;keyword-based Web-image annotation;keyword-filled facet table;ontology;quantified conceptual keyword relation;Competitive intelligence;Computer science;Facial animation;History;Image retrieval;Indium tin oxide;Intelligent systems;Ontologies;Software systems;Vocabulary;based image annotation;conceptual keyword relations;facet tables;folksonomy;ontology},
}

@InProceedings{antonellis_etal_2009,
  author    = {Ioannis Antonellis and Hector Garcia-Molina and Jawed Karim},
  title     = {Tagging with Queries: How and Why?},
  booktitle = {Second ACM International Conference on Web Search and Data Mining WSDM 2009, Late Breaking Results Session},
  year      = {2009},
  month     = {February},
  publisher = {Infolab},
  abstract  = {Web search queries capture the information need of search engine users. Search engines store these queries in their logs and analyze them to guide their search results.

In this work, we argue that not only a search engine can benefit from data stored in these logs, but also the web users. We first show how clickthrough logs can be collected in a distributed fashion using the http referer field in web server access logs. We then perform a set of experiments to study the information value of search engine queries when treated as "tags" or "labels" for the web pages that both appear as a result and the user actually clicks on. We ask how much extra information these query
tags provide for web pages by comparing them to tags from the del.icio.us bookmarking site and to the pagetext. We find that query tags can provide substantially many (on average 250 tags per URL), new tags (on average 125 tags per URL are not present in the pagetext) for a large fraction of the Web.},
  keywords  = {query tags, query logs, click logs, tagging, web search, web navigation, rank1},
  url       = {http://ilpubs.stanford.edu:8090/903/},
}

@InProceedings{musto_etal_2009,
  author    = {Musto, Cataldo and Narducci, Fedelucio and De Gemmis, Marco and Lops, Pasquale and Semeraro, Giovanni},
  title     = {STaR: A Social Tag Recommender System},
  booktitle = {Proceedings of the 2009th International Conference on ECML PKDD Discovery Challenge - Volume 497},
  year      = {2009},
  series    = {ECMLPKDDDC'09},
  pages     = {215--227},
  address   = {Aachen, Germany, Germany},
  publisher = {CEUR-WS.org},
  acmid     = {3056164},
  keywords  = {collaborative tagging systems, folksonomies, recommender systems, web 2.0},
  location  = {Bled, Slovenia},
  numpages  = {13},
  url       = {http://dl.acm.org/citation.cfm?id=3056147.3056164},
}

@InProceedings{panagakis_kotropoulos_2011,
  author    = {Y. Panagakis and C. Kotropoulos},
  title     = {Automatic music mood classification via Low-Rank Representation},
  booktitle = {2011 19th European Signal Processing Conference},
  year      = {2011},
  pages     = {689-693},
  month     = {Aug},
  abstract  = {The problem of automatic music mood classification is addressed by resorting to low-rank representation of slow auditory spectro-temporal modulations. Recently, it has been shown that if each data class is linearly spanned by a subspace of unknown dimensions and the data are noiseless, the lowest-rank representation (LRR) of a set of test vector samples with respect to a set of training vector samples has the nature of being both dense for within-class affinities and almost zero for between-class affinities. Consequently, the LRR exactly reveals the classification of the data, resulting into the so-called Low-Rank Representation-based Classification (LRRC). The performance of the LRRC is compared against three well-known classifiers, namely the Sparse Representations-based Classifier, Support Vector Machines, and Nearest Neighbor classifiers for music mood classification by conducting experiments on the MTV and the Soundtracks180 datasets. The experimental results validate the effectiveness of the LRRC among the classifiers that is compared to.},
  issn      = {2076-1465},
  keywords  = {music;pattern classification;support vector machines;LRRC;MTV;Soundtracks180 datasets;automatic music mood classification;low-rank representation-based classification;nearest neighbor classifiers;slow auditory spectro-temporal modulations;sparse representations-based classifier;support vector machines;Accuracy;Modulation;Mood;Psychoacoustic models;Support vector machines;Training;Vectors},
  url       = {https://www.eurasip.org/Proceedings/Eusipco/Eusipco2011/papers/1569427041.pdf},
}

@Conference{ilhan_oegueduecue_2011,
  author          = {Ilhan, N. and Ögüdücü, Ş.G.},
  title           = {Tag recommendation based on user's behavior in collaborative tagging systems},
  year            = {2011},
  volume          = {1},
  pages           = {570-573},
  note            = {cited By},
  abstract        = {Social bookmarking Web sites allow users submitting their resources and labeling them with arbitrary keywords, called tags, to create folksonomies. Tag recommendation is an important element of collaborative tagging systems which aims at providing relevant information to users by proposing a set of tags to each newly posted resource. In this paper, we focus on the task of tag recommendation when a user examines a document based on the user's tagging behavior. We explore the use of this semantic relationship in modeling the user tagging behavior. The experiments are performed on the data set obtained from a social bookmarking site. Our experimental result show that our method is efficient in modeling users' tagging behavior and it can be used to recommend tags for resources.},
  affiliation     = {Istanbul Technical University, Department of Computer Engineering, Maslak, Istanbul, 34469, Turkey},
  author_keywords = {Collaborative tagging; Recommender systems; Social network analysis; Tag suggestions},
  document_type   = {Conference Paper},
  journal         = {ICAART 2011 - Proceedings of the 3rd International Conference on Agents and Artificial Intelligence},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-79960109961&partnerID=40&md5=c25b8bea2544da0c19f2d6ab82aa0a2f},
}

@InProceedings{belem_etal_2012,
  author    = {Bel{\'e}m, Fabiano Muniz and Martins, Eder Ferreira and Almeida, Jussara Marques and Gon\c{c}alves, Marcos Andr{\'e}},
  title     = {Exploiting Relevance, Novelty and Diversity in Tag Recommendation},
  booktitle = {Proceedings of the 18th Brazilian Symposium on Multimedia and the Web},
  year      = {2012},
  series    = {WebMedia '12},
  pages     = {297--300},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2382699},
  doi       = {10.1145/2382636.2382699},
  isbn      = {978-1-4503-1706-1},
  keywords  = {diversidade, novidade, recomenda??o de tags},
  location  = {S\&\#227;o Paulo/SP, Brazil},
  numpages  = {4},
  url       = {http://doi.acm.org/10.1145/2382636.2382699},
}

@InProceedings{oliveira_etal_2012,
  author    = {Oliveira, Vitor and Gomes, Guilherme and Bel{\'e}m, Fabiano and Brand\~{a}o, Wladmir and Almeida, Jussara and Ziviani, Nivio and Gon\c{c}alves, Marcos},
  title     = {Automatic Query Expansion Based on Tag Recommendation},
  booktitle = {Proceedings of the 21st ACM International Conference on Information and Knowledge Management},
  year      = {2012},
  series    = {CIKM '12},
  pages     = {1985--1989},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2398557},
  doi       = {10.1145/2396761.2398557},
  isbn      = {978-1-4503-1156-4},
  keywords  = {query expansion, tag recommendation, wikipedia},
  location  = {Maui, Hawaii, USA},
  numpages  = {5},
  url       = {http://doi.acm.org/10.1145/2396761.2398557},
}

@Article{lu_etal_2013,
  author   = {W. Lu and J. Li and T. Li and W. Guo and H. Zhang and J. Guo},
  title    = {Web Multimedia Object Classification Using Cross-Domain Correlation Knowledge},
  journal  = {IEEE Transactions on Multimedia},
  year     = {2013},
  volume   = {15},
  number   = {8},
  pages    = {1920-1929},
  month    = {Dec},
  issn     = {1520-9210},
  abstract = {Given a collection of web images with the corresponding textual descriptions, in this paper, we propose a novel cross-domain learning method to classify these web multimedia objects by transferring the correlation knowledge among different information sources. Here, the knowledge is extracted from unlabeled objects through unsupervised learning and applied to perform supervised classification tasks. To mine more meaningful correlation knowledge, instead of using commonly used visual words in the traditional bag-of-visual-words (BoW) model, we discover higher level visual components (words and phrases) to incorporate the spatial and semantic information into our image representation model, i.e., bag-of-visual-phrases (BoP). By combining the enriched visual components with the textual words, we calculate the frequently co-occurring pairs among them to construct a cross-domain correlated graph in which the correlation knowledge is mined. After that, we investigate two different strategies to apply such knowledge to enrich the feature space where the supervised classification is performed. By transferring such knowledge, our cross-domain transfer learning method can not only handle large scale web multimedia objects, but also deal with the situation that the textual descriptions of a small portion of web images are missing. Empirical experiments on two different datasets of web multimedia objects are conducted to demonstrate the efficacy and effectiveness of our proposed cross-domain transfer learning method.},
  doi      = {10.1109/TMM.2013.2280895},
  keywords = {Internet;correlation methods;graph theory;image classification;image representation;knowledge acquisition;multimedia computing;text analysis;unsupervised learning;BoP;BoW;Web image collection;Web multimedia object classification;bag-of-visual-phrases;bag-of-visual-words model;cross-domain correlated graph;cross-domain correlation knowledge;cross-domain transfer learning method;feature space;image representation model;information sources;knowledge extraction;supervised classification tasks;textual descriptions;unsupervised learning;visual components;Correlation;Feature extraction;Knowledge engineering;Learning systems;Multimedia communication;Semantics;Visualization;Bag-of-Visual-Phrases Model;Correlation Knowledge;Cross-Domain;Multimedia Object Classification;Transfer Learning},
}

@InProceedings{wang_etal_2013a,
  author    = {T. Wang and H. Wang and G. Yin and C. X. Ling and X. Li and P. Zou},
  title     = {Mining Software Profile across Multiple Repositories for Hierarchical Categorization},
  booktitle = {2013 IEEE International Conference on Software Maintenance},
  year      = {2013},
  pages     = {240-249},
  month     = {Sept},
  abstract  = {The large amounts of software repositories over the Internet are fundamentally changing the traditional paradigms of software maintenance. Efficient categorization of the massive projects for retrieving the relevant software in these repositories is of vital importance for Internet-based maintenance tasks such as solution searching, best practices learning and so on. Many previous works have been conducted on software categorization by mining source code or byte code, which are only verified on relatively small collections of projects with coarse-grained categories or clusters. However, Internet-based software maintenance requires finer-grained, more scalable and language-independent categorization approaches. In this paper, we propose a novel approach to hierarchically categorize software projects based on their online profiles across multiple repositories. We design a SVM-based categorization framework to classify the massive number of software hierarchically. To improve the categorization performance, we aggregate different types of profile attributes from multiple repositories and design a weighted combination strategy which assigns greater weights to more important attributes. Extensive experiments are carried out on more than 18,000 projects across three repositories. The results show that our approach achieves significant improvements by using weighted combination, and the overall precision, recall and F-Measure can reach 71.41%, 65.60% and 68.38% in appropriate settings. Compared to the previous work, our approach presents competitive results with 123 finer-grained and multi-layered categories. In contrast to those using source code or byte code, our approach is more effective for large-scale and language-independent software categorization.},
  doi       = {10.1109/ICSM.2013.35},
  issn      = {1063-6773},
  keywords  = {Internet;data mining;pattern classification;software maintenance;software management;support vector machines;F-Measure;Internet-based software maintenance;SVM-based categorization;byte code;hierarchical categorization;language-independent software categorization;large-scale software categorization;online profiles;recall;software hierarchical classification;software profile mining;software projects;software repositories;source code;support vector machines;weighted combination strategy;Collaboration;Data mining;Databases;Internet;Servers;Software maintenance;Hierarchical Categorization;Software Profile;Software Repository},
}

@InProceedings{jung_etal_2013,
  author    = {Y. Jung and Y. Cho and Y. M. Park and T. Lee},
  title     = {Automatic Tagging of Functional-Goals for Goal-Driven Semantic Service Discovery},
  booktitle = {2013 IEEE Seventh International Conference on Semantic Computing},
  year      = {2013},
  pages     = {212-219},
  month     = {Sept},
  abstract  = {With the growing number of Web services available on the Web, finding services that match user's query becomes more difficult. To deal with the problem, numerous approaches employed semantic techniques in terms of annotation (or tagging) and service discovery algorithms. However, the problem of semantic service discovery based on the identification of fine-grained goals of users and services is still challenging due to the lack of semantic information in Web services. To enable the goal-driven semantic service discovery, we propose an automatic functional-goals tagging approach which employs a set of natural language processing (NLP) procedures based on the descriptions of Web services. In addition, to check the effectiveness of the functional-goals tagged, we designed a goal-driven semantic service discovery algorithm and compared it with other approaches: keyword-based, ontology-based, and topic-based service discovery. As results, our proposed goal-driven semantic service discovery achieved 75.7% of precision and 59.6% of recall (finally, F1=62%) that outperform other discovery approaches.},
  doi       = {10.1109/ICSC.2013.45},
  keywords  = {Web services;natural language processing;ontologies (artificial intelligence);query processing;NLP;Web services;annotation;automatic functional-goals tagging approach;goal-driven semantic service discovery algorithm;keyword-based service discovery;natural language processing;ontology-based service discovery;semantic information;semantic techniques;topic-based service discovery;user query;Data mining;Google;Ontologies;Semantics;Tagging;Videos;Web services;automatic tagging;functional-goal;goal-driven semantic service discovery;web service},
}

@Article{xu_etal_2014a,
  author        = {Zheng Xu and Yunhuai Liu and Lin Mei and Chuanping Hu and Lan Chen},
  title         = {Generating temporal semantic context of concepts using web search engines},
  journal       = {Journal of Network and Computer Applications},
  year          = {2014},
  volume        = {43},
  pages         = {42 - 55},
  issn          = {1084-8045},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.jnca.2014.04.002},
  keywords      = {Temporal semantic context, Semantic annotation, Content analysis, Web mining},
  url           = {http://www.sciencedirect.com/science/article/pii/S108480451400085X},
}

@Conference{gueye_etal_2014,
  author        = {Gueye, M. and Abdessalem, T. and Naacke, H.},
  title         = {A parameter-free algorithm for an optimized tag recommendation list size},
  year          = {2014},
  pages         = {233-240},
  note          = {cited By},
  abstract      = {Tag recommendation is a major aspect of collaborative tagging systems. It aims to recommend suitable tags to a user for tagging an item. One of its main challenges is the effectiveness of its recommendations. Existing works focus on techniques for retrieving the most relevant tags to give beforehand, with a fixed number of tags in each recommended list. In this paper, we try to optimize the number of recommended tags in order to improve the efficiency of the recommendations. We propose a parameter-free algorithm for determining the optimal size of the recommended list. Thus we introduced some relevance measures to find the most relevant sublist from a given list of recommended tags. More precisely, we improve the quality of our recommendations by discarding some unsuitable tags and thus adjusting the list size. Our solution is an add-on one, which can be implemented on top of many kinds of tag recommenders. The experiments we did on five datasets, using four categories of tag recommenders, demonstrate the efficiency of our technique. For instance, the algorithm we propose outperforms the results of the task 2 of the ECML PKDD Discovery Challenge 2009 1. By using the same tag recommender than the winners of the contest, we reach a F1 measure of 0.366 while the latter got 0.356. Thus, our solution yields significant improvements on the lists obtained from the tag recommenders. Copyright © 2014 ACM.},
  affiliation   = {Université Cheikh Anta Diop, BP. Fann Dakar, Senegal; Institut Telecom - Telecom, ParisTech, 46, rue Barrault 75013, Paris, France; LIP6 - UPMC Sorbonne Universités, 4 place Jussieu 75005, Paris, France},
  document_type = {Conference Paper},
  doi           = {10.1145/2645710.2645727},
  journal       = {RecSys 2014 - Proceedings of the 8th ACM Conference on Recommender Systems},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84908884108&doi=10.1145%2f2645710.2645727&partnerID=40&md5=612d66bd2afa9326ef0395247a44d1dc},
}

@Article{duong_etal_2015,
  author        = {Trong Hai Duong and Ngoc Thanh Nguyen and Hai Bang Truong and Van Huan Nguyen},
  title         = {A collaborative algorithm for semantic video annotation using a consensus-based social network analysis},
  journal       = {Expert Systems with Applications},
  year          = {2015},
  volume        = {42},
  number        = {1},
  pages         = {246 - 258},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2014.07.046},
  keywords      = {Video annotation, Semantic web, Social network, Consensus},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417414004539},
}

@Article{khan_etal_2017,
  author        = {Umer Khan and Lars Schmidt-Thieme and Alexandros Nanopoulos},
  title         = {Collaborative SVM classification in scale-free peer-to-peer networks},
  journal       = {Expert Systems with Applications},
  year          = {2017},
  volume        = {69},
  pages         = {74 - 86},
  issn          = {0957-4174},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.eswa.2016.10.008},
  keywords      = {Distributed, Classification, SVM, P2P, Skew},
  url           = {http://www.sciencedirect.com/science/article/pii/S0957417416305395},
}

@InProceedings{wang_blei_2011,
  author    = {Wang, Chong and Blei, David M.},
  title     = {Collaborative Topic Modeling for Recommending Scientific Articles},
  booktitle = {Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining},
  year      = {2011},
  series    = {KDD '11},
  pages     = {448--456},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {2020480},
  doi       = {10.1145/2020408.2020480},
  isbn      = {978-1-4503-0813-7},
  keywords  = {collaborative filtering, latent structure interpretation, scientific article recommendation, topic modeling},
  location  = {San Diego, California, USA},
  numpages  = {9},
  url       = {http://doi.acm.org/10.1145/2020408.2020480},
}

@InProceedings{shen_fan_2010,
  author    = {Shen, Yi and Fan, Jianping},
  title     = {Leveraging Loosely-tagged Images and Inter-object Correlations for Tag Recommendation},
  booktitle = {Proceedings of the 18th ACM International Conference on Multimedia},
  year      = {2010},
  series    = {MM '10},
  pages     = {5--14},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1873956},
  doi       = {10.1145/1873951.1873956},
  isbn      = {978-1-60558-933-6},
  keywords  = {loosely-tagged images, multi-task structured svm, multiple instance learning, object correlation network},
  location  = {Firenze, Italy},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1873951.1873956},
}

@Comment{jabref-meta: databaseType:bibtex;}
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@InProceedings{marlow_etal_2006,
  author    = {Marlow, Cameron and Naaman, Mor and Boyd, Danah and Davis, Marc},
  title     = {HT06, Tagging Paper, Taxonomy, Flickr, Academic Article, to Read},
  booktitle = {Proceedings of the Seventeenth Conference on Hypertext and Hypermedia},
  year      = {2006},
  series    = {HYPERTEXT '06},
  pages     = {31--40},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1149949},
  doi       = {10.1145/1149941.1149949},
  isbn      = {1-59593-417-0},
  keywords  = {Flickr, categorization, classification, folksonomy, incentives, models, research, social networks, social software, tagging systems, tagsonomy, taxonomy},
  location  = {Odense, Denmark},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1149941.1149949},
}

@Article{golder_huberman_2006,
  author          = {Golder, S.A. and Huberman, B.A.},
  title           = {Usage patterns of collaborative tagging systems},
  journal         = {Journal of Information Science},
  year            = {2006},
  volume          = {32},
  number          = {2},
  pages           = {198-208},
  note            = {cited By},
  abstract        = {Collaborative tagging describes the process by which many users add metadata in the form of keywords to shared content. Recently, collaborative tagging has grown in popularity on the web, on sites that allow users to tag bookmarks, photographs and other content. In this paper we analyze the structure of collaborative tagging systems as well as their dynamic aspects. Specifically, we discovered regularities in user activity, tag frequencies, kinds of tags used, bursts of popularity in bookmarking and a remarkable stability in the relative proportions of tags within a given URL. We also present a dynamic model of collaborative tagging that predicts these stable patterns and relates them to imitation and shared knowledge. © CILIP.},
  affiliation     = {Information Dynamics Lab., HP Labs., Palo Alto, CA, United States; HP Labs., MS 1139, 1501 Page Mill Rd, Palo Alto, CA 94304, United States},
  author_keywords = {Bookmarks; Collaborative tagging; Del.icio.us; Folksonomy; Sharing; Web},
  document_type   = {Article},
  doi             = {10.1177/0165551506062337},
  keywords        = {rank3},
  source          = {Scopus},
  url             = {http://journals.sagepub.com/doi/pdf/10.1177/0165551506062337},
}

@InProceedings{halpin_etal_2007,
  author    = {Halpin, Harry and Robu, Valentin and Shepherd, Hana},
  title     = {The Complex Dynamics of Collaborative Tagging},
  booktitle = {Proceedings of the 16th International Conference on World Wide Web},
  year      = {2007},
  series    = {WWW '07},
  pages     = {211--220},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1242602},
  doi       = {10.1145/1242572.1242602},
  isbn      = {978-1-59593-654-7},
  keywords  = {collaborative filtering, complex systems, delicious, emergent semantics, power laws, tagging},
  location  = {Banff, Alberta, Canada},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1242572.1242602},
}

@Article{bischoff_etal_2010a,
  author   = {Kerstin Bischoff and Claudiu S. Firan and Wolfgang Nejdl and Raluca Paiu},
  title    = {Bridging the gap between tagging and querying vocabularies: Analyses and applications for enhancing multimedia IR},
  journal  = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year     = {2010},
  volume   = {8},
  number   = {2},
  pages    = {97 - 109},
  issn     = {1570-8268},
  note     = {Bridging the Gap—Data Mining and Social Network Analysis for Integrating Semantic Web and Web 2.0 The Future of Knowledge Dissemination: The Elsevier Grand Challenge for the Life Sciences},
  doi      = {http://dx.doi.org/10.1016/j.websem.2010.04.004},
  keywords = {Web 2.0, Tag analysis, Web information retrieval, Knowledge discovery, Tag recommendation},
  url      = {http://www.sciencedirect.com/science/article/pii/S1570826810000284},
}

@InProceedings{ames_naaman_2007,
  author    = {Ames, Morgan and Naaman, Mor},
  title     = {Why We Tag: Motivations for Annotation in Mobile and Online Media},
  booktitle = {Proceedings of the SIGCHI Conference on Human Factors in Computing Systems},
  year      = {2007},
  series    = {CHI '07},
  pages     = {971--980},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1240772},
  doi       = {10.1145/1240624.1240772},
  isbn      = {978-1-59593-593-9},
  keywords  = {CSCW, annotation, cameraphone, collection organization, digital photographs, incentives, location-aware, motivations, photo sharing, tagging, rank2},
  location  = {San Jose, California, USA},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1240624.1240772},
}

@Article{sen_riedl_2011,
  author   = {S. Sen and J. Riedl},
  title    = {Folksonomy Formation},
  journal  = {Computer},
  year     = {2011},
  volume   = {44},
  number   = {5},
  pages    = {97-101},
  month    = {May},
  issn     = {0018-9162},
  abstract = {Tags, words or short phrases attached to items on the Internet, organize the social Web. Unlike expert-maintained taxonomies such as the Dewey Decimal Classification, tags evolve organically based on the collective action of individual users. The collection of tags in a tagging system, called a folksonomy, can vary widely from site to site. The paper mentions that the combination of eager taggers and intelligent algorithms that nurture effective tagging behavior promises a bright future for tagging systems.},
  doi      = {10.1109/MC.2011.147},
  keywords = {social networking (online);Internet;folksonomy;intelligent algorithms;social Web;tagging behavior;tagging systems;tsg collection;Algorithms;Social network services;Taxonomy;Thesauri;Folksonomies;GroupLens;Social computing;Tagging, rank2},
}

@Misc{wal_2005_broad_and_narrow,
  author = {Wal, V. d.},
  title  = {Explaining and showing broad and narrow folksonomies},
  year   = {2005},
  url    = {http://www.vanderwal.net/random/entrysel.php?blog=1635},
}

@Misc{wal_2005_folksonomy,
  author = {Wal, V. d.},
  title  = {Folksonomy Explanations},
  year={2005},
  url = {http://www.vanderwal.net/random/entrysel.php?blog=1622}
}

@InProceedings{elhussein_nakata_2012,
  author    = {M. Elhussein and K. Nakata},
  title     = {Analysing the Factors That Influence Tag Choice Based on Semiotic Analysis and Activity Theory},
  booktitle = {2012 International Conference on Social Informatics},
  year      = {2012},
  pages     = {96-105},
  month     = {Dec},
  abstract  = {Social tagging has become popular around the Internet as well as in research. The main idea behind tagging is to allow users to provide metadata to the web content from their perspective to facilitate categorization and retrieval. There are many factors that influence users' tag choice. Many studies have been conducted to reveal these factors by analysing tagging data. This paper uses two theories to identify these factors, namely the semiotics theory and activity theory. The former treats tags as signs and the latter treats tagging as an activity. The theoretical analysis produced a framework that was used to identify a number of factors. These factors can be considered as categories that can be consulted to influence user tagging choice in order to support particular tagging behaviour, such as cross-lingual tagging.},
  doi       = {10.1109/SocialInformatics.2012.78},
  keywords  = {data analysis;social networking (online);user interfaces;Internet;Web content categorization;Web content retrieval;activity theory;cross-lingual tagging;meta data;semiotics theory;social tagging;tagging data analysis;user tag choice;Activity Theory;Social tagging;semiotic ladder;semiotics;tag choice;user collaboration, rank1},
}

@Article{bollen_halpin_2013,
  author          = {Bollen, D. and Halpin, H.},
  title           = {An experimental analysis of suggestions in collaborative tagging},
  journal         = {Web Intelligence and Agent Systems},
  year            = {2013},
  volume          = {11},
  number          = {1},
  pages           = {41-53},
  note            = {cited By},
  abstract        = {Most tagging systems support the user in the tag selection process by providing tag suggestions, or recommendations, based on a popularity measurement of tags other users provided when tagging the same resource, such as web-page. In this paper we investigate the influence of tag suggestions on the emergence of power-law distributions as a result of collaborative tag behavior. Although previous research has already shown that power-laws emerge in tagging systems, the cause of why power-law distributions emerge is not understood empirically. The majority of theories and mathematical models of tagging found in the literature assume that the emergence of power-laws in tagging systems is mainly driven by the imitation behavior of users when observing tag suggestions provided by the user interface of the tagging system. This imitation behavior leads to a feedback loop in which some tags are reinforced, which is also known as the 'rich get richer' or a preferential attachment model. We present experimental results showing that the power-law distribution forms when tag suggestions are not presented to the users, and the power-law distribution does not hold when tag suggestions are presented to the user. Furthermore, we show that the real effect of tag suggestions is rather subtle; the power-law distribution that would naturally occur without tag suggestions is 'compressed' if tag suggestions are given to the user, resulting in a shorter long tail and a 'compressed' top of the power-law distribution. We show that tag suggestions by themselves do not account for the formation of power-law distributions in tagging systems. © 2013-IOS Press and the authors. All rights reserved.},
  affiliation     = {Faculty of Innovation Sciences and Industrial Engineering, Eindhoven University of Technology, Den Dolech 2, 5600 MB Eindhoven, Netherlands; School of Informatics, University of Edinburgh, United Kingdom},
  author_keywords = {Distributed information systems; information retrieval; user interfaces},
  document_type   = {Article},
  doi             = {10.3233/WIA-130261},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84880193043&doi=10.3233%2fWIA-130261&partnerID=40&md5=ba0a9574fe05508b555fa4ceda23c04a},
}

@InProceedings{karpathy_etal_2014,
  author    = {A. Karpathy and G. Toderici and S. Shetty and T. Leung and R. Sukthankar and L. Fei-Fei},
  title     = {Large-Scale Video Classification with Convolutional Neural Networks},
  booktitle = {2014 IEEE Conference on Computer Vision and Pattern Recognition},
  year      = {2014},
  pages     = {1725-1732},
  month     = {June},
  abstract  = {Convolutional Neural Networks (CNNs) have been established as a powerful class of models for image recognition problems. Encouraged by these results, we provide an extensive empirical evaluation of CNNs on large-scale video classification using a new dataset of 1 million YouTube videos belonging to 487 classes. We study multiple approaches for extending the connectivity of a CNN in time domain to take advantage of local spatio-temporal information and suggest a multiresolution, foveated architecture as a promising way of speeding up the training. Our best spatio-temporal networks display significant performance improvements compared to strong feature-based baselines (55.3% to 63.9%), but only a surprisingly modest improvement compared to single-frame models (59.3% to 60.9%). We further study the generalization performance of our best model by retraining the top layers on the UCF-101 Action Recognition dataset and observe significant performance improvements compared to the UCF-101 baseline model (63.3% up from 43.9%).},
  doi       = {10.1109/CVPR.2014.223},
  issn      = {1063-6919},
  keywords  = {image classification;image motion analysis;multimedia computing;neural nets;social networking (online);spatiotemporal phenomena;video signal processing;CNN;UCF-101 action recognition dataset;UCF-101 baseline model;YouTube videos;convolutional neural networks;feature-based baselines;image recognition problems;local spatiotemporal information;spatiotemporal networks;video classification;Computational modeling;Computer architecture;Feature extraction;Spatial resolution;Streaming media;Training;action;classification;convolutional;dataset;large-scale;network;neural;recognition;sports;video},
}

@Article{mathes_2004,
  author        = {Adam Mathes},
  title         = {Folksonomies - cooperative classification and communication through shared metadata},
  journal       = {Computer Mediated Communication},
  year          = {2004},
  month         = {December},
  __markedentry = {[felipe:1]},
  abstract      = {This paper examines user-‍generated metadata as implemented and applied in two web services designed to share and organize digital media to better understand grassroots classification. Metadata - data about data - allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In libraries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated professionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without specialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described documents was heralded by SGML, the WWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well - often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-‍created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community.},
  url           = {http://adammathes.com/academic/computer-mediated-communication/folksonomies.html},
}

@Article{hammond_etal_2005,
  author  = {T. Hammond, T. Hannay, B. Lund, J. Scott},
  title   = {Social Bookmarking Tools (I): A General Review},
  journal = {D-Lib Magazin},
  year    = {2005},
  volume  = {11},
  number  = {4},
  month   = {April},
  url     = {http://www.dlib.org/dlib/april05/hammond/04hammond.html},
}

@Article{golder_huberman_2005,
  author    = {Scott A. Golder and Bernardo A. Huberman},
  title     = {The Structure of Collaborative Tagging Systems},
  journal   = {CoRR},
  year      = {2005},
  volume    = {abs/cs/0508082},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-cs-0508082},
  keywords  = {rank3},
  timestamp = {Wed, 07 Jun 2017 14:40:39 +0200},
  url       = {http://arxiv.org/abs/cs/0508082},
}

@Article{steels_2006,
  author          = {Steels, L.},
  title           = {Collaborative tagging as distributed cognition},
  journal         = {Pragmatics and Cognition},
  year            = {2006},
  volume          = {14},
  number          = {2},
  pages           = {287-292},
  note            = {cited By},
  abstract        = {The paper discusses recent developments in web technologies based on collaborative tagging. This approach is seen as a tremendously powerful way to coordinate the ontologies and views of a large number of individuals, thus constituting the most successful tool for distributed cognition so far. © John Benjamins Publishing Company.},
  affiliation     = {Vrije Universiteit, Brussel, Belgium; Sony CSL, Paris, France; 6 Rue Amyot, 75005 Paris, France},
  author_keywords = {Collaborative tagging; Distributed cognition; Semiotic dynamics},
  document_type   = {Article},
  doi             = {10.1075/pc.14.2.09ste},
  source          = {Scopus},
  url             = {http://www.jasonmorrison.net/iakm/cited/Steels_collaborative_tagging_distributed_cognition.pdf},
}

@Article{steels_2006a,
  author   = {L. Steels},
  title    = {Semiotic Dynamics for Embodied Agents},
  journal  = {IEEE Intelligent Systems},
  year     = {2006},
  volume   = {21},
  number   = {3},
  pages    = {32-38},
  month    = {Jan},
  issn     = {1541-1672},
  abstract = {Artificial intelligence explores the many different aspects of intelligence like a meandering stream carving out rivers, lakes, and deltas in an endless magnificent landscape. Each time new vistas on intelligence open up, we build new technologies to explore them and find new types of applications. In this article, the author briefly illustrate the current study of semiotic dynamics, the resulting technologies, and the field's impact on current and future intelligent systems applications},
  doi      = {10.1109/MIS.2006.58},
  keywords = {artificial intelligence;software agents;artificial intelligence;embodied agents;intelligent systems applications;semiotic dynamics;Adaptive systems;Artificial intelligence;Computer science;Humans;Intelligent agent;Laboratories;Natural languages;Psychology;Steel;Tagging;artificial intelligence;intelligent agents;intelligent systems;multiagent systems;semiotic dynamics, rank2},
}

@Article{mika_2007,
  author   = {Peter Mika},
  title    = {Ontologies are us: A unified model of social networks and semantics},
  journal  = {Web Semantics: Science, Services and Agents on the World Wide Web},
  year     = {2007},
  volume   = {5},
  number   = {1},
  pages    = {5 - 15},
  issn     = {1570-8268},
  note     = {Selected Papers from the International Semantic Web Conference},
  doi      = {http://dx.doi.org/10.1016/j.websem.2006.11.002},
  keywords = {Knowledge representation, Folksonomies, Tagging},
  url      = {http://www.sciencedirect.com/science/article/pii/S1570826806000552},
}

@Article{al-khalifa_davis_2007,
  author          = {Al-Khalifa, H.S. and Davis, H.C.},
  title           = {Exploring the value of folksonomies for creating semantic metadata},
  journal         = {International Journal on Semantic Web and Information Systems},
  year            = {2007},
  volume          = {3},
  number          = {1},
  pages           = {12-38},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Finding good keywords to describe resources is an on-going problem. Typically, we select such words manually from a thesaurus of terms, or they are created using automatic keyword extraction techniques. Folksonomies are an increasingly well-populated source of unstructured tags describing Web resources. This article explores the value of the folksonomy tags as a potential source of keyword metadata by examining the relationship between folksonomies, community produced annotations, and keywords extracted by machines. The experiment has been carried out in two ways: subjectively, by asking two human indexers to evaluate the quality of the generated keywords from both systems; and automatically, by measuring the percentage of overlap between the folksonomy set and machine generated keywords set. The results of this experiment show that the folksonomy tags agree more closely with the human generated keywords than those automatically generated. The results also showed that the trained indexers preferred the semantics of folksonomy tags compared to keywords extracted automatically. These results can be considered as evidence for the strong relationship of folksonomies to the human indexer's mindset, demonstrating that folksonomies used in the del.icio. us bookmarking service are a potential source for generating semantic metadata to annotate Web resources. Copyright © 2007, IGI Global.},
  affiliation     = {University of Southampton, United Kingdom},
  author_keywords = {Collaborative tagging; Folksonomy; Keyword extraction; Metadata; Social bookmarking services; Tags; Web technologies},
  document_type   = {Article},
  source          = {Scopus},
  url             = {http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.105.3788&rep=rep1&type=pdf},
}

@Article{chi_mytkowicz_2007,
  author        = {Chi, Ed and Mytkowicz, Todd},
  title         = {Understanding navigability of social tagging systems},
  year          = {2007},
  month         = {08},
  __markedentry = {[felipe:1]},
  keywords      = {rank1},
  url           = {https://www.researchgate.net/publication/228338967_Understanding_navigability_of_social_tagging_systems},
}

@InProceedings{rodrigues_etal_2008,
  author        = {E. M. Rodrigues and N. Milic-Frayling and B. Fortuna},
  title         = {Social Tagging Behaviour in Community-Driven Question Answering},
  booktitle     = {2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology},
  year          = {2008},
  volume        = {1},
  pages         = {112-119},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {On-line community services such as Live QnA and Yahoo! Answers enable their members to ask questions and have them answered by the community. The questions are labelled by the users to facilitate search, navigation, and recommendations. In this paper we provide an in-depth analysis of the question labelling practices by contrasting the use of community generated tags in the Live QnA service with the use of topic categories from a fixed taxonomy in the Yahoo! Answers service. We found that community tagging is related to higher levels of social interactions amongst users. Analysis of the most frequently used community tags reveals that active users may establish strong social ties around specific tags. Furthermore, the discriminative value of individual community tags can be low since the corresponding questions may cover a variety of topics. Thus, appropriate care needs to be taken when designing search, browsing, and recommender features for question discovery.},
  doi           = {10.1109/WIIAT.2008.138},
  keywords      = {social networking (online);text analysis;Live QnA service;community-driven question answering;online community service;social interaction;social tagging behaviour;Buildings;Intelligent agent;Labeling;Navigation;Portals;Social network services;Tagging;Taxonomy;Voting;Web search;Q&A;community tags;online communities;question types;social network analysis;topic classification, rank1},
}

@Article{jaeschke_etal_2008,
  author          = {Jäschke, R. and Marinho, L. and Hotho, A. and Schmidt-Thieme, L. and Stumme, G.},
  title           = {Tag recommendations in social bookmarking systems},
  journal         = {AI Communications},
  year            = {2008},
  volume          = {21},
  number          = {4},
  pages           = {231-247},
  note            = {cited By},
  abstract        = {Collaborative tagging systems allow users to assign keywords - so called "tags" - to resources. Tags are used for navigation, finding resources and serendipitous browsing and thus provide an immediate benefit for users. These systems usually include tag recommendation mechanisms easing the process of finding good tags for a resource, but also consolidating the tag vocabulary across users. In practice, however, only very basic recommendation strategies are applied. In this paper we evaluate and compare several recommendation algorithms on large-scale real life datasets: an adaptation of user-based collaborative filtering, a graph-based recommender built on top of the FolkRank algorithm, and simple methods based on counting tag occurrences. We show that both FolkRank and collaborative filtering provide better results than non-personalized baseline methods. Moreover, since methods based on counting tag occurrences are computationally cheap, and thus usually preferable for real time scenarios, we discuss simple approaches for improving the performance of such methods. We show, how a simple recommender based on counting tags from users and resources can perform almost as good as the best recommender. © 2008 - IOS Press and the authors. All rights reserved.},
  affiliation     = {Knowledge and Data Engineering Group (KDE), University of Kassel, Kassel, Germany; Information Systems and Machine Learning Lab. (ISMLL), University of Hildesheim, Hildesheim, Germany; Research Center L3S, Hannover, Germany; Knowledge and Data Engineering Group (KDE), University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany},
  author_keywords = {Folksonomies; Ranking; Recommender systems; Social bookmarking},
  document_type   = {Article},
  doi             = {10.3233/AIC-2008-0438},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://www.dmir.uni-wuerzburg.de/staff/hotho/?tx_extbibsonomycsl_publicationlist%5BuserName%5D=hotho&tx_extbibsonomycsl_publicationlist%5BintraHash%5D=955bcf14f3272ba6eaf3dadbef6c0b10&tx_extbibsonomycsl_publicationlist%5BfileName%5D=jaeschke2008tag.pdf&tx_extbibsonomycsl_publicationlist%5Baction%5D=download&tx_extbibsonomycsl_publicationlist%5Bcontroller%5D=Document&cHash=1ce8364259e4230f9eda76aa4c37b862},
}

@Conference{gemmell_etal_2009,
  author          = {Gemmell, J. and Ramezani, M. and Schimoler, T. and Christiansen, L. and Mobasher, B.},
  title           = {The impact of ambiguity and redundancy on tag recommendation in folksonomies},
  year            = {2009},
  pages           = {45-52},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging applications have become a popular tool allowing Internet users to manage online resources with tags. Most collaborative tagging applications permit unsupervised tagging resulting in tag ambiguity in which a single tag has many different meanings and tag redundancy in which several tags have the same meaning. Common metrics for evaluating tag recommenders may overestimate the utility of ambiguous tags or ignore the appropriateness of redundant tags. Ambiguity and redundancy may even burden the user with additional effort by requiring them to clarify an annotation or forcing them to distinguish between highly related items. In this paper we demonstrate that ambiguity and redundancy impede the evaluation and performance of tag recommenders. Five tag recommendation strategies based on popularity, collaborative filtering and link analysis are explored. We use a cluster-based approach to define ambiguity and redundancy and provide extensive evaluation on three real world datasets. Copyright 2009 ACM.},
  affiliation     = {Center for Web Intelligence, School of Computing, DePaul University, Chicago, IL, United States},
  author_keywords = {Ambiguity; Folksonomies; Recommender system; Redundancy},
  document_type   = {Conference Paper},
  doi             = {10.1145/1639714.1639724},
  journal         = {RecSys'09 - Proceedings of the 3rd ACM Conference on Recommender Systems},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1639714.1639724},
}

@Article{li_lu_2008,
  author   = {Q. Li and S. C. Y. Lu},
  title    = {Collaborative Tagging Applications and Approaches},
  journal  = {IEEE MultiMedia},
  year     = {2008},
  volume   = {15},
  number   = {3},
  pages    = {14-21},
  month    = {July},
  issn     = {1070-986X},
  abstract = {This article reviews the state of the art in collaborative tagging, its current challenges, and potential methods for resolving these challenges, with a special focus on Web-based, user-generated content applications.},
  doi      = {10.1109/MMUL.2008.54},
  keywords = {Internet;identification technology;multimedia computing;Web-based content applications;collaborative tagging applications;user-generated content applications;Blogs;Digital audio broadcasting;Information resources;Internet;MySpace;Online Communities/Technical Collaboration;Ontologies;Tagging;User-generated content;YouTube},
}

@InProceedings{zhang_etal_2009,
  author    = {Zhang, Shaoke and Farooq, Umer and Carroll, John M.},
  title     = {Enhancing Information Scent: Identifying and Recommending Quality Tags},
  booktitle = {Proceedings of the ACM 2009 International Conference on Supporting Group Work},
  year      = {2009},
  series    = {GROUP '09},
  pages     = {1--10},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1531676},
  doi       = {10.1145/1531674.1531676},
  isbn      = {978-1-60558-500-0},
  keywords  = {quality tags, sense-making, social bookmarking},
  location  = {Sanibel Island, Florida, USA},
  numpages  = {10},
  url       = {http://doi.acm.org/10.1145/1531674.1531676},
}

@InProceedings{huang_zhou_2010,
  author        = {X. Huang and Y. Zhou},
  title         = {An Asymmetric Similarity Measure for Tag Clustering on Flickr},
  booktitle     = {2010 12th International Asia-Pacific Web Conference},
  year          = {2010},
  pages         = {171-177},
  month         = {April},
  __markedentry = {[felipe:1]},
  abstract      = {Web 2.0 tools and environments have made tagging, the act of assigning keywords to on-line objects, a popular way to annotate shared resources. The success of now-prominent tagging systems makes tagging "the natural way for people to classify objects as well as an attractive way to discover new material". One of the most challenging problems is to harvest the semantics from these systems, which can support a number of applications, including tag clustering and tag recommendation. We conduct detailed studies on different types of tag relations and tag similarity measures, and propose a scalable measure that we name Reliability Factor Similarity Measure (RFSM). We compare it with two other measures having similar scalability by integrating them into hierarchical clustering methods and performing tag clustering on a subset of Flickr data. The results suggest that RFSM outperforms those two measures when it is applies for tag clustering purpose. We also present an alternative way of utilizing discovered tag relations to set up tag refining rules in order to deal with some noise in the initial tag sets, which can in turn improve the precision of tag relations.},
  doi           = {10.1109/APWeb.2010.65},
  keywords      = {Web sites;identification technology;pattern clustering;Flickr;Web 2.0 tools;asymmetric similarity measure;hierarchical clustering method;keyword assigning;reliability factor similarity measure;shared resource annotation;tag clustering;tag recommendation;tag relations;tag similarity measures;Australia;Clustering algorithms;Clustering methods;Conducting materials;Costs;Information technology;Performance evaluation;Scalability;Tagging;Vocabulary;Clustering;Folksonomy;Reliability Factor Similarity Measure;Tag;Web 2.0, rank1},
}

@InProceedings{dattolo_etal_2010,
  author        = {A. Dattolo and F. Ferrara and C. Tasso},
  title         = {The role of tags for recommendation: A survey},
  booktitle     = {3rd International Conference on Human System Interaction},
  year          = {2010},
  pages         = {548-555},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging is an innovative and powerful mechanism introduced with Web 2.0: it shifts the task of classifying resources from a reduced set of knowledge engineers to the wide set of Web users. Users of social tagging systems define personal classifications which can be used by other peers for browsing available resources. However, due to the absence of rules for managing the tagging process, and to the lack of predefined schemas or structures for inserting metadata and relationships among tags, current user generated classifications dop not produce sound taxonomies. This is a strong limitation which prevents an effective and informed resource sharing. For this reason researchers are modeling innovative recommender systems capable to better support tagging, browsing, and searching for new resources. This paper is a survey which discusses the role of tags in recommender systems: starting from social tagging systems, we analyze various techniques for suggesting content and we introduce the approaches exploited for proposing tags for classifying resources, considering both personalized and not-personalized recommendation.},
  doi           = {10.1109/HSI.2010.5514515},
  issn          = {2158-2246},
  keywords      = {Internet;meta data;pattern classification;personal computing;recommender systems;user interfaces;Web 2.0;informed resource sharing;metadata;personal classification;recommender system;social tagging;Decision support systems;Helium;Knowledge engineering;Mercury (metals);Power engineering and energy;Recommender systems;Resource management;Tagging;Taxonomy;Tag;personalization;recommender system;social tagging, rank1},
}

@InProceedings{zhang_etal_2009a,
  author    = {Zhang, Dell and Mao, Robert and Li, Wei},
  title     = {The Recurrence Dynamics of Social Tagging},
  booktitle = {Proceedings of the 18th International Conference on World Wide Web},
  year      = {2009},
  series    = {WWW '09},
  pages     = {1205--1206},
  address   = {New York, NY, USA},
  publisher = {ACM},
  acmid     = {1526930},
  doi       = {10.1145/1526709.1526930},
  isbn      = {978-1-60558-487-4},
  keywords  = {folksonomy, social tagging, web 2.0},
  location  = {Madrid, Spain},
  numpages  = {2},
  url       = {http://doi.acm.org/10.1145/1526709.1526930},
}

@Article{benz_etal_2010,
  author          = {Benz, D. and Hotho, A. and Jäschke, R. and Krause, B. and Mitzlaff, F. and Schmitz, C. and Stumme, G.},
  title           = {The social bookmark and publication management system bibsonomy},
  journal         = {VLDB Journal},
  year            = {2010},
  volume          = {19},
  number          = {6},
  pages           = {849-875},
  note            = {cited By},
  abstract        = {Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research. © 2010 Springer-Verlag.},
  affiliation     = {Knowledge and Data Engineering Group, Research Center for Information Systems Design, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany; Data Mining and Information Retrieval Group, University of Würzburg, Am Hubland, 97074 Würzburg, Germany; L3S Research Center, Appelstr. 9a, 30167 Hannover, Germany},
  author_keywords = {BibSonomy; Collaborative tagging; Data mining; Folksonomy; Web 2.0},
  document_type   = {Article},
  doi             = {10.1007/s00778-010-0208-4},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {https://link.springer.com/article/10.1007%2Fs00778-010-0208-4},
}

@Conference{lipczak_milios_2010a,
  author          = {Lipczak, M. and Milios, E.},
  title           = {The impact of resource title on tags in collaborative tagging systems},
  year            = {2010},
  pages           = {179-188},
  note            = {cited By},
  abstract        = {Collaborative tagging systems are popular tools for organization, sharing and retrieval of web resources. Their success is due to their freedom and simplicity of use. To post a resource, the user should only define a set of tags that would position the resource in the system's data structure -folksonomy. This data structure can serve as a rich source of information about relations between tags and concepts they represent. To make use of information collaboratively added to folksonomies, we need to understand how users make tagging decisions. Three factors that are believed to inuence user tagging decisions are: the tags used by other users, the organization of user's personal repository and the knowledge model shared between users. In our work we examine the role of another potential factor - resource title. Despite all the advantages of tags, tagging is a tedious process. To minimize the effort, users are likely to tag with keywords that are easily available. We show that resource title, as a source of useful tags, is easy to access and comprehend. Given a choice of two tags with the same meaning, users are likely to be inuenced by their presence in the title. However, a factor that seems to have stronger impact on users' tagging decisions is maintaining the consistency of the personal profile of tags. The results of our study reveal a new, less idealistic picture of collaborative tagging systems, in which the collaborative aspect seems to be less important than personal gains and convenience. Copyright 2010 ACM.},
  affiliation     = {Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada},
  author_keywords = {Collaborative tagging; Folksonomies; Modeling},
  document_type   = {Conference Paper},
  doi             = {10.1145/1810617.1810648},
  journal         = {HT'10 - Proceedings of the 21st ACM Conference on Hypertext and Hypermedia},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=1810617.1810648},
}

@Article{gupta_etal_2010,
  author     = {Gupta, Manish and Li, Rui and Yin, Zhijun and Han, Jiawei},
  title      = {Survey on Social Tagging Techniques},
  journal    = {SIGKDD Explor. Newsl.},
  year       = {2010},
  volume     = {12},
  number     = {1},
  pages      = {58--72},
  month      = nov,
  issn       = {1931-0145},
  acmid      = {1882480},
  address    = {New York, NY, USA},
  doi        = {10.1145/1882471.1882480},
  issue_date = {June 2010},
  keywords   = {bookmarking, collaborative tagging, distributed classification, ethnoclassification, folk classification, folk taxonomy, folksonomy, social classification, social indexing, social tagging, tagging, rank1},
  numpages   = {15},
  publisher  = {ACM},
  url        = {http://doi.acm.org/10.1145/1882471.1882480},
}

@InProceedings{zhang_etal_2011,
  author    = {M. Zhang and C. Ding and Z. Liao},
  title     = {Tensor Fold-in Algorithms for Social Tagging Prediction},
  booktitle = {2011 IEEE 11th International Conference on Data Mining},
  year      = {2011},
  pages     = {1254-1259},
  month     = {Dec},
  abstract  = {Social tagging predictions involve the co occurrence of users, items and tags. The tremendous growth of users require the recommender system to produce tag recommendations for millions of users and items at any minute. The triplets of users, items and tags are most naturally described by a 3D tensor, and tensor decomposition-based algorithms can produce high quality recommendations. However, each day, thousands of new users are added to the system and the decompositions must be updated daily in a online fashion. In this paper, we provide analysis of the new user problem, and present fold-in algorithms for Tucker, Para Fac, and Low-order tensor decompositions. We show that these algorithm can very efficiently compute the needed decompositions. We evaluate the fold-in algorithms experimentally on several datasets and the results demonstrate the effectiveness of these algorithms.},
  doi       = {10.1109/ICDM.2011.142},
  issn      = {1550-4786},
  keywords  = {data mining;recommender systems;social networking (online);solid modelling;tensors;3D tensor decomposition based algorithm;ParaFac decomposition;Tucker decomposition;low-order tensor decomposition;recommender system;social tagging prediction;tag recommendation;tensor fold-in algorithm;Accuracy;Algorithm design and analysis;Matrix decomposition;Prediction algorithms;Predictive models;Tagging;Tensile stress;Graph Mining;Recommender System;Social Network, rank2},
}

@InProceedings{theocharis_etal_2011,
  author    = {A. Theocharis and M. Pierce and G. Tzanetakis},
  title     = {An Empirical Investigation of Stacking for Music Tag Annotation},
  booktitle = {2011 10th International Conference on Machine Learning and Applications and Workshops},
  year      = {2011},
  volume    = {1},
  pages     = {90-95},
  month     = {Dec},
  abstract  = {Automatic tag annotation is one of the most important problems in multimedia information retrieval. It has been motivated by the large amount of unstructured tag annotation data provided by internet users and can be viewed as a variation of multi-label classification with special characteristics and constraints. Stacking is a technique in which the outputs (binary or probabilistic) of a set of binary classifiers (one for each tag) are used as input to a second stage of classification that attempts to exploit latent relationships between tags. This technique (known under a variety of names) has been used in a variety of multimedia tag annotation systems. In this paper we survey these approaches, clarify how stacking system are structured, and empirically investigate stacking using a variety of classifier combinations in the context of tagging pieces of music.},
  doi       = {10.1109/ICMLA.2011.30},
  keywords  = {Internet;information analysis;information retrieval;multimedia computing;music;pattern classification;Internet users;binary classifiers;multilabel classification;multimedia information retrieval;multimedia tag annotation systems;music piece tagging;music tag annotation stacking;unstructured tag annotation data;Multimedia communication;Niobium;Stacking;Support vector machines;Tagging;Training;Vectors;automatic tag annotation;classification;multi-label classificaiton;multimedia information retrieval;music information retrieval;stacking},
}

@Article{lipczak_milios_2011,
  author          = {Lipczak, M. and Milios, E.},
  title           = {Efficient tag recommendation for real-life data},
  journal         = {ACM Transactions on Intelligent Systems and Technology},
  year            = {2011},
  volume          = {3},
  number          = {1},
  note            = {cited By},
  abstract        = {Despite all of the advantages of tags as an easy and flexible information management approach, tagging is a cumbersome task. A set of descriptive tags has to be manually entered by users whenever they post a resource. This process can be simplified by the use of tag recommendation systems. Their objective is to suggest potentially useful tags to the user. We present a hybrid tag recommendation system together with a scalable, highly efficient system architecture. The system is able to utilize user feedback to tune its parameters to specific characteristics of the underlying tagging system and adapt the recommendation models to newly added content. The evaluation of the system on six real-life datasets demonstrated the system's ability to combine tags from various sources (e.g., resource content or tags previously used by the user) to achieve the best quality of recommended tags. It also confirmed the importance of parameter tuning and content adaptation. A series of additional experiments allowed us to better understand the characteristics of the system and tagging datasets and to determine the potential areas for further system development. © 2011 ACM.},
  affiliation     = {Faculty of Computer Science, Dalhousie University, Halifax, B3H 1W5, Canada},
  art_number      = {2},
  author_keywords = {Broad folksonomies; Collaborative tagging; Folksonomies; Hybrid systems; Narrow folksonomies; Tag recommendation},
  document_type   = {Article},
  doi             = {10.1145/2036264.2036266},
  keywords        = {rank2},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2036264.2036266},
}

@Conference{alepidou_etal_2011,
  author          = {Alepidou, Z.I. and Vavliakis, K.N. and Mitkas, P.A.},
  title           = {A semantic tag recommendation framework for collaborative tagging systems},
  booktitle       = {2011 IEEE Third International Conference on Privacy, Security, Risk and Trust and 2011 IEEE Third International Conference on Social Computing},
  year            = {2011},
  pages           = {633-636},
  month           = {Oct},
  note            = {cited By},
  abstract        = {In this work we focus on folksonomies. Our goal is to develop techniques that coordinate information processing, by taking advantage of user preferences, in order to automatically produce semantic tag recommendations. To this end, we propose a generalized tag recommendation framework that conveys the semantics of resources according to different user profiles. We present the integration of various models that take into account content, historic values, user preferences and tagging behavior to produce accurate personalized tag recommendations. Based on this information we build several Bayesian models, we evaluate their performance, and we discuss differences in accuracy with respect to semantic matching criteria, and other approaches. © 2011 IEEE.},
  affiliation     = {Electrical and Computer Engineering, Aristotle University of Thessaloniki, Greece; CERTH, Informatics and Telematics Institute, Thessaloniki, Greece},
  art_number      = {6113185},
  author_keywords = {Folksonomy; Personalization; Recommendation; Semantic evaluation; Tagging},
  document_type   = {Conference Paper},
  doi             = {10.1109/PASSAT/SocialCom.2011.170},
  journal         = {Proceedings - 2011 IEEE International Conference on Privacy, Security, Risk and Trust and IEEE International Conference on Social Computing, PASSAT/SocialCom 2011},
  keywords        = {Bayes methods;groupware;pattern matching;recommender systems;Bayesian model;account content;collaborative tagging system;folksonomies;generalized tag recommendation framework;historic values;information processing;performance evaluation;personalized tag recommendation;semantic matching criteria;semantic tag recommendation framework;tagging behavior;user preferences;Bayesian methods;Conferences;History;Machine learning;Semantics;Tagging;Training;folksonomy;personalization;recommendation;semantic evaluation;tagging},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84856150279&doi=10.1109%2fPASSAT%2fSocialCom.2011.170&partnerID=40&md5=65b040e8250fd5e51027b95e33f1939e},
}

@Article{schifanella_etal_2010,
  author    = {Rossano Schifanella and Alain Barrat and Ciro Cattuto and Benjamin Markines and Filippo Menczer},
  title     = {Folks in Folksonomies: Social Link Prediction from Shared Metadata},
  journal   = {CoRR},
  year      = {2010},
  volume    = {abs/1003.2281},
  bibsource = {dblp computer science bibliography, http://dblp.org},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1003-2281},
  timestamp = {Wed, 07 Jun 2017 14:40:04 +0200},
  url       = {http://arxiv.org/abs/1003.2281},
}

@Article{chevalier_etal_2011,
  author          = {Chevalier, M. and Dattolo, A. and Hubert, G. and Pitassi, E.},
  title           = {Information retrieval and folksonomies together for recommender systems},
  journal         = {Lecture Notes in Business Information Processing},
  year            = {2011},
  volume          = {85 LNBIP},
  pages           = {172-183},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {The powerful and democratic activity of social tagging allows the wide set of Web users to add free annotations on resources. Tags express user interests, preferences and needs, but also automatically generate folksonomies. They can be considered as gold mine, especially for e-commerce applications, in order to provide effective recommendations. Thus, several recommender systems exploit folksonomies in this context. Folksonomies have also been involved in many information retrieval approaches. In considering that information retrieval and recommender systems are siblings, we notice that few works deal with the integration of their approaches, concepts and techniques to improve recommendation. This paper is a first attempt in this direction. We propose a trail through recommender systems, social Web, e-commerce and social commerce, tags and information retrieval: an overview on the methodologies, and a survey on folksonomy-based information retrieval from recommender systems point of view, delineating a set of open and new perspectives. © 2011 Springer-Verlag.},
  affiliation     = {IRIT, Université P. Sabatier, 118 rte de Narbonne, F-31062 Toulouse, France; University of Udine, Via delle Scienze 206, I-33100 Udine, Italy},
  author_keywords = {e-commerce; Folksonomy; Information Retrieval; Recommendation},
  document_type   = {Conference Paper},
  doi             = {10.1007/978-3-642-23014-1_15},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-80052908188&doi=10.1007%2f978-3-642-23014-1_15&partnerID=40&md5=7f71bd93c40f0f54d47d8a61f6a7d140},
}

@Article{siddique_maqbool_2012,
  author        = {F. Siddique and O. Maqbool},
  title         = {Enhancing comprehensibility of software clustering results},
  journal       = {IET Software},
  year          = {2012},
  volume        = {6},
  number        = {4},
  pages         = {283-295},
  month         = {August},
  issn          = {1751-8806},
  __markedentry = {[felipe:1]},
  abstract      = {As requirements of organisations change, so do the software systems within them. When changes are carried out under tough deadlines, software developers often do not follow software engineering principles, which results in deteriorated structure of the software. A badly structured system is difficult to understand for further changes. To improve structure, re-modularisation may be carried out. Clustering techniques have been used to facilitate automatic re-modularisation. However, clusters produced by clustering algorithms are difficult to comprehend unless they are labelled appropriately. Manual assignment of labels is tiresome, thus efforts should be made towards automatic cluster label assignment. In this study, the authors focus on facilitating comprehension of software clustering results by automatically assigning meaningful labels to clusters. To assign labels, the authors use term weighting schemes borrowed from the domain of information retrieval and text categorisation. Although some term weighting schemes have been used by researchers for software cluster labelling, there is a need to analyse the term weighting schemes and related issues to identify the strengths and weaknesses of these schemes for software cluster labelling. In this context, the authors analyse the behaviour of seven well-known term weighting schemes. Also, they perform the experiments on five software systems to identify software characteristics which affect the labelling behaviour of the term weighting schemes.},
  doi           = {10.1049/iet-sen.2012.0027},
  keywords      = {formal specification;formal verification;information retrieval;organisational aspects;pattern clustering;text analysis;automatic cluster label assignment;automatic remodularisation;information retrieval;labels manual assignment;organisations change;software characteristics;software cluster labelling;software clustering algorithm;software developers;software engineering principles;software systems;term weighting schemes;text categorisation},
  url           = {http://ieeexplore.ieee.org/document/6322850/},
}

@InProceedings{uzun-per_2012,
  author        = {M. Uzun-Per},
  title         = {User representation in social bookmarking websites},
  booktitle     = {2012 International Conference on Computer Systems and Industrial Informatics},
  year          = {2012},
  pages         = {1-6},
  month         = {Dec},
  __markedentry = {[felipe:1]},
  abstract      = {As social networking websites become more and more popular, the necessity to serve users of these websites is increased. Since all such social websites are user-centric, representing users with a good model directly changes the results of the applications. There are many studies which consider users in their models. In this paper, we analyzed such papers and collected different user representation models. Although there is no study which considers time parameter in user representation, we included the time parameter in one of the models. We evaluated the models for two social bookmarking website datasets by visualizing them as a graph with social network analysis, and by calculating f-measure scores for a tag recommendation system. As a result, tag property is considered as the best property to represent a user in such a website.},
  doi           = {10.1109/ICCSII.2012.6454622},
  keywords      = {collaborative filtering;data visualisation;network theory (graphs);recommender systems;social networking (online);data visualization;f-measure scores;social bookmarking Web sites;social network analysis;social networking Web sites;tag recommendation system;time parameter;user representation models;user-centric user representation;Analytical models;Clustering algorithms;Covariance matrix;Matrix decomposition;Social network services;Sparse matrices;Tagging;social bookmarking websites;tagging;user representation},
}

@InProceedings{katakis_etal_2012,
  author        = {I. Katakis and G. Pallis and M. D. Dikaiakos and O. Onoufriou},
  title         = {Automated Tagging for the Retrieval of Software Resources in Grid and Cloud Infrastructures},
  booktitle     = {2012 12th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (ccgrid 2012)},
  year          = {2012},
  pages         = {628-635},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {A key challenge for Grid and Cloud infrastructures is to make their services easily accessible and attractive to end-users. In this paper we introduce tagging capabilities to the Miner soft system, a powerful tool for software search and discovery in order to help end-users locate application software suitable to their needs. Miner soft is now able to predict and automatically assign tags to software resources it indexes. In order to achieve this, we model the problem of tag prediction as a multi-label classification problem. Using data extracted from production-quality Grid and Cloud computing infrastructures, we evaluate an important number of multi-label classifiers and discuss which one and with what settings is the most appropriate for use in the particular problem.},
  doi           = {10.1109/CCGrid.2012.66},
  keywords      = {cloud computing;grid computing;identification technology;Miner soft system;application software;automated tagging;cloud computing infrastructures;multilabel classification problem;multilabel classifiers;production quality grid computing infrastructures;software resource retrieval;software resources;software search;tag prediction;tagging capability;Cloud computing;Indexes;Libraries;Machine learning;Tagging;Training;classification;cloud;grid;information retrieval;machine learning;mining;software;tagging},
}

@Article{jaeschke_etal_2012,
  author        = {Jäschke, R. and Hotho, A. and Mitzlaff, F. and Stumme, G.},
  title         = {Challenges in Tag recommendations for collaborative tagging systems},
  journal       = {Intelligent Systems Reference Library},
  year          = {2012},
  volume        = {32},
  pages         = {65-87},
  note          = {cited By},
  __markedentry = {[felipe:1]},
  abstract      = {Originally introduced by social bookmarking systems, collaborative tagging, or social tagging, has been widely adopted by many web-based systems like wikis, e-commerce platforms, or social networks. Collaborative tagging systems allow users to annotate resources using freely chosen keywords, so called tags. Those tags help users in finding/retrieving resources, discovering new resources, and navigating through the system. The process of tagging resources is laborious. Therefore, most systems support their users by tag recommender components that recommend tags in a personalized way. The Discovery Challenges 2008 and 2009 of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD) tackled the problem of tag recommendations in collaborative tagging systems. Researchers were invited to test their methods in a competition on datasets from the social bookmark and publication sharing system BibSonomy. Moreover, the 2009 challenge included an online task where the recommender systems were integrated into BibSonomy and provided recommendations in real time. In this chapter we review, evaluate and summarize the submissions to the two Discovery Challenges and thus lay the groundwork for continuing research in this area. © Springer-Verlag Berlin Heidelberg 2012.},
  affiliation   = {Knowledge and Data Engineering Group, University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany; Data Mining and Information Retrieval Group at LS VI, University of Würzburg, Am Hubland, 97074 Würzburg, Germany},
  document_type = {Article},
  doi           = {10.1007/978-3-642-25694-3_3},
  keywords      = {rank2},
  source        = {Scopus},
  url           = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84881393421&doi=10.1007%2f978-3-642-25694-3_3&partnerID=40&md5=ff92ab9e4138fe1dd251e4a55d1c9d36},
}

@Conference{dellschaft_staab_2012,
  author          = {Dellschaft, K. and Staab, S.},
  title           = {Measuring the influence of tag recommenders on the indexing quality in tagging systems},
  year            = {2012},
  pages           = {73-82},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {In this paper, we investigate a methodology for measuring the influence of tag recommenders on the indexing quality in collaborative tagging systems. We propose to use the inter-resource consistency as an indicator of indexing quality. The inter-resource consistency measures the degree to which the tag vectors of indexed resources reflect how the users understand the resources. We use this methodology for evaluating how tag recommendations coming from (1) the popular tags at a resource or from (2) the user's own vocabulary influence the indexing quality. We show that recommending popular tags decreases the indexing quality and that recommending the user's own vocabulary increases the indexing quality. Copyright 2012 ACM.},
  affiliation     = {Inst. for Web Science and Technologies (WeST), Universität Koblenz-Landau, Koblenz, Germany},
  author_keywords = {Collaborative tagging; Indexing quality; Tag recommenders},
  document_type   = {Conference Paper},
  doi             = {10.1145/2309996.2310009},
  journal         = {HT'12 - Proceedings of 23rd ACM Conference on Hypertext and Social Media},
  source          = {Scopus},
  url             = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-84864073691&doi=10.1145%2f2309996.2310009&partnerID=40&md5=b8ae238f7a9fd466914601d5c5b95bbd},
}

@Article{vig_etal_2012,
  author        = {Vig, Jesse and Sen, Shilad and Riedl, John},
  title         = {The Tag Genome: Encoding Community Knowledge to Support Novel Interaction},
  journal       = {ACM Trans. Interact. Intell. Syst.},
  year          = {2012},
  volume        = {2},
  number        = {3},
  pages         = {13:1--13:44},
  month         = sep,
  issn          = {2160-6455},
  __markedentry = {[felipe:1]},
  acmid         = {2362395},
  address       = {New York, NY, USA},
  articleno     = {13},
  doi           = {10.1145/2362394.2362395},
  issue_date    = {September 2012},
  keywords      = {Tagging, conversational recommenders, data mining, information retrieval, machine learning, recommender systems},
  numpages      = {44},
  publisher     = {ACM},
  url           = {http://doi.acm.org/10.1145/2362394.2362395},
}

@Conference{doerfel_jaeschke_2013,
  author          = {Doerfel, S. and Jäschke, R.},
  title           = {An analysis of tag-recommender evaluation procedures},
  year            = {2013},
  pages           = {343-346},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Since the rise of collaborative tagging systems on the web, the tag recommendation task suggesting suitable tags to users of such systems while they add resources to their collection has been tackled. However, the (offine) evaluation of tag recommendation algorithms usually suffers from dificulties like the sparseness of the data or the cold start problem for new resources or users. Previous studies therefore often used so-called post-cores (specific subsets of the original datasets) for their experiments. In this paper, we conduct a large-scale experiment in which we analyze different tag recommendation algorithms on different cores of three real-world datasets. We show, that a recommender's performance depends on the particular core and explore correlations between performances on different cores. © 2013 ACM.},
  affiliation     = {University of Kassel, Wilhelmshöher Allee 73, 34121 Kassel, Germany; L3S Research Center, Appelstraße 4, 30167 Hannover, Germany},
  author_keywords = {Core; Evaluation; Graph; Recommender},
  document_type   = {Conference Paper},
  doi             = {10.1145/2507157.2507222},
  journal         = {RecSys 2013 - Proceedings of the 7th ACM Conference on Recommender Systems},
  keywords        = {rank1},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2507157.2507222},
}

@Article{subramaniyaswamy_etal_2013,
  author        = {Subramaniyaswamy, V. and Vijayakumar, V. and Indragandhi, V.},
  title         = {A Review of Ontology-Based Tag Recommendation Approaches},
  journal       = {International Journal of Intelligent Systems},
  year          = {2013},
  volume        = {28},
  number        = {11},
  pages         = {1054--1071},
  issn          = {1098-111X},
  __markedentry = {[felipe:1]},
  abstract      = {Tag recommender schemes suggest related tags for an untagged resource and better tag suggestions to tagged resources. Tagging is very important if the user identifies the tag that is more precise to use in searching interesting blogs. There is no clear information regarding the meaning of each tag in a tagging process. An user can use various tags for the same content, and he can also use new tags for an item in a blog. When the user selects tags, the resultant metadata may comprise homonyms and synonyms. This may cause an improper relationship among items and ineffective searches for topic information. The collaborative tag recommendation allows a set of freely selected text keywords as tags assigned by users. These tags are imprecise, irrelevant, and misleading because there is no control over the tag assignment. It does not follow any formal guidelines to assist tag generation, and tags are assigned to resources based on the knowledge of the users. This causes misspelled tags, multiple tags with the same meaning, bad word encoding, and personalized words without common meaning. This problem leads to miscategorization of items, irrelevant search results, wrong prediction, and their recommendations. Tag relevancy can be judged only by a specific user. These aspects could provide new challenges and opportunities to its tag recommendation problem. This paper reviews the challenges to meet the tag recommendation problem. A brief comparison between existing works is presented, which we can identify and point out the novel research directions. The overall performance of our ontology-based recommender systems is favorably compared to other systems in the literature.},
  doi           = {10.1002/int.21616},
  keywords      = {rank3},
  url           = {http://dx.doi.org/10.1002/int.21616},
}

@Article{wu_zou_2014,
  author        = {Zhenyu Wu and Ming Zou},
  title         = {Modeling social tagging using latent interaction potential},
  journal       = {Physica A: Statistical Mechanics and its Applications},
  year          = {2014},
  volume        = {413},
  pages         = {125 - 133},
  issn          = {0378-4371},
  __markedentry = {[felipe:1]},
  doi           = {http://dx.doi.org/10.1016/j.physa.2014.06.048},
  keywords      = {Social tagging system, Latent interaction potential, Collective intelligence, rank1},
  url           = {http://www.sciencedirect.com/science/article/pii/S0378437114005238},
}

@InCollection{batley_2014,
  title         = {4 - Classifying digital resources},
  booktitle     = {Classification in Theory and Practice},
  publisher     = {Chandos Publishing},
  year          = {2014},
  editor        = {Batley, Sue},
  pages         = {143 - 171},
  address       = {Oxford},
  isbn          = {978-1-84334-785-9},
  __markedentry = {[felipe:1]},
  abstract      = {Abstract: This chapter examines how classification theories can be applied in the digital information environment. Vocabulary control and thesaurus construction are studied in the context of creating subject trees as structured browsing environments. Then practical, user-centred taxonomy creation and maintenance are explored. The concept of ontology in the context of information management is explained. Finally, collaborative classification and the impact of user-generated content to provide an additional level of organisation to web-based information are discussed. },
  doi           = {https://doi.org/10.1533/9781780634661.143},
  key           = {tagkey2014143},
  keywords      = {collaborative classification, folksonomies, needs analysis, ontologies, subject trees, tagging, tag clouds, task analysis, taxonomies, thesaurus construction, user-centred design, user models, vocabulary control, web directories},
  url           = {http://www.sciencedirect.com/science/article/pii/B9781843347859500041},
}

@Article{6153150,
  author        = {I. Ivanov and P. Vajda and J. S. Lee and T. Ebrahimi},
  title         = {In Tags We Trust: Trust modeling in social tagging of multimedia content},
  journal       = {IEEE Signal Processing Magazine},
  year          = {2012},
  volume        = {29},
  number        = {2},
  pages         = {98-107},
  month         = {March},
  issn          = {1053-5888},
  __markedentry = {[felipe:1]},
  abstract      = {Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.},
  doi           = {10.1109/MSP.2011.942345},
  keywords      = {information retrieval;multimedia computing;social networking (online);trusted computing;unsolicited e-mail;multimedia content retrieval;online social networks;social tagging;spam annotations;trust modeling;Content management;Information analysis;Information retrieval;Multimedia communication;Noise measurement;Online services;Search problems;Social network services;Tagging},
}

@Article{ivanov_etal_2012,
  author   = {I. Ivanov and P. Vajda and J. S. Lee and T. Ebrahimi},
  title    = {In Tags We Trust: Trust modeling in social tagging of multimedia content},
  journal  = {IEEE Signal Processing Magazine},
  year     = {2012},
  volume   = {29},
  number   = {2},
  pages    = {98-107},
  month    = {March},
  issn     = {1053-5888},
  abstract = {Tagging in online social networks is very popular these days, as it facilitates search and retrieval of multimedia content. However, noisy and spam annotations often make it difficult to perform an efficient search. Users may make mistakes in tagging and irrelevant tags and content may be maliciously added for advertisement or self-promotion. This article surveys recent advances in techniques for combatting such noise and spam in social tagging. We classify the state-of-the-art approaches into a few categories and study representative examples in each. We also qualitatively compare and contrast them and outline open issues for future research.},
  doi      = {10.1109/MSP.2011.942345},
  keywords = {information retrieval;multimedia computing;social networking (online);trusted computing;unsolicited e-mail;multimedia content retrieval;online social networks;social tagging;spam annotations;trust modeling;Content management;Information analysis;Information retrieval;Multimedia communication;Noise measurement;Online services;Search problems;Social network services;Tagging},
}

@InProceedings{beyer_pinzger_2015,
  author        = {S. Beyer and M. Pinzger},
  title         = {Synonym Suggestion for Tags on Stack Overflow},
  booktitle     = {2015 IEEE 23rd International Conference on Program Comprehension},
  year          = {2015},
  pages         = {94-103},
  month         = {May},
  __markedentry = {[felipe:1]},
  abstract      = {The amount of diverse tags used to classify posts on Stack Overflow increased in the last years to more than 38,000 tags. Many of these tags have the same or similar meaning. Stack Overflow provides an approach to reduce the amount of tags by allowing privileged users to manually create synonyms. However, currently exist only 2,765 synonym-pairs on Stack Overflow that is quite low compared to the total number of tags. To comprehend how synonym-pairs are built, we manually analyzed the tags and how the synonyms could be created automatically. Based on our findings, we then present TSST, a tag synonym suggestion tool, that outputs a ranked list of possible synonyms for each input tag. We first evaluated TSST with the 2,765 approved synonym-pairs of Stack Overflow. For 88.4% of the tags TSST finds the correct synonyms, for 72.2% the correct synonym is within the top 10 suggestions. In addition, we applied TSST to 10 randomly selected Android related tags and evaluated the suggested synonyms with 20 Android app developers in an online survey. Overall, in 80% of their ratings, developers found an adequate synonym suggested by TSST.},
  doi           = {10.1109/ICPC.2015.18},
  issn          = {1092-8138},
  keywords      = {Internet;question answering (information retrieval);Android app;Stack Overflow;Web 2.0;social bookmarking;synonym-pairs;tag synonym suggestion tool;Accuracy;Androids;Databases;Humanoid robots;Java;Radiation detectors;Terminology;Stack Overflow;synonyms;tags, rank1},
}

@InCollection{bordes_etal_2013,
  author    = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
  title     = {Translating Embeddings for Modeling Multi-relational Data},
  booktitle = {Advances in Neural Information Processing Systems 26},
  publisher = {Curran Associates, Inc.},
  year      = {2013},
  editor    = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
  pages     = {2787--2795},
  url       = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf},
}

@Article{font_etal_2015,
  author          = {Font, F. and Serrà, J. and Serra, X.},
  title           = {Analysis of the impact of a tag recommendation system in a real-world Folksonomy},
  journal         = {ACM Transactions on Intelligent Systems and Technology},
  year            = {2015},
  volume          = {7},
  number          = {1},
  note            = {cited By},
  __markedentry   = {[felipe:1]},
  abstract        = {Collaborative tagging systems have emerged as a successful solution for annotating contributed resources to online sharing platforms, facilitating searching, browsing, and organizing their contents. To aid users in the annotation process, several tag recommendation methods have been proposed. It has been repeatedly hypothesized that these methods should contribute to improving annotation quality and reducing the cost of the annotation process. It has been also hypothesized that these methods should contribute to the consolidation of the vocabulary of collaborative tagging systems. However, to date, no empirical and quantitative result supports these hypotheses. In this work, we deeply analyze the impact of a tag recommendation system in the folksonomy of Freesound, a real-world and large-scale online sound sharing platform. Our results suggest that tag recommendation effectively increases vocabulary sharing among users of the platform. In addition, tag recommendation is shown to contribute to the convergence of the vocabulary as well as to a partial increase in the quality of annotations. However, according to our analysis, the cost of the annotation process does not seem to be effectively reduced. Our work is relevant to increase our understanding about the nature of tag recommendation systems and points to future directions for the further development of those systems and their analysis. © 2015 ACM.},
  affiliation     = {Music Technology Group, Universitat Pompeu Fabra, Barcelona, Spain; Artificial Intelligence Research Institute (IIIA-CSIC), Spanish National Research Council, Bellaterra, Spain},
  art_number      = {6},
  author_keywords = {Experimentation; Measurement; Verification},
  document_type   = {Article},
  doi             = {10.1145/2743026},
  keywords        = {rank3},
  source          = {Scopus},
  url             = {http://dl.acm.org/citation.cfm?doid=2830012.2743026},
}

@Article{godoy_corbellini_2016,
  author        = {Godoy, Daniela and Corbellini, Alejandro},
  title         = {Folksonomy-Based Recommender Systems: A State-of-the-Art Review},
  journal       = {International Journal of Intelligent Systems},
  year          = {2016},
  volume        = {31},
  number        = {4},
  pages         = {314--346},
  issn          = {1098-111X},
  __markedentry = {[felipe:1]},
  abstract      = {Collaborative tagging systems, also known as folksonomies, have grown in popularity over the Web on account of their simplicity to organize several types of content (e.g., Web pages, pictures, and video) using open-ended tags. The rapid adoption of these systems has led to an increasing amount of users providing information about themselves and, at the same time, a growing and rich corpus of social knowledge that can be exploited by recommendation technologies. In this context, tripartite relationships between users, resources, and tags contained in folksonomies set new challenges for knowledge discovery approaches to be applied for the purposes of assisting users through recommendation systems. This review aims at providing a comprehensive overview of the literature in the field of folksonomy-based recommender systems. Current recommendation approaches stemming from fields such as user modeling, collaborative filtering, content, and link-analysis are reviewed and discussed to provide a starting point for researchers in the field as well as explore future research lines.},
  doi           = {10.1002/int.21753},
  keywords      = {rank3},
  url           = {http://dx.doi.org/10.1002/int.21753},
}

@Article{belem_etal_2017,
  author        = {Belém, Fabiano M. and Almeida, Jussara M. and Gonçalves, Marcos A.},
  title         = {A survey on tag recommendation methods},
  journal       = {Journal of the Association for Information Science and Technology},
  year          = {2017},
  volume        = {68},
  number        = {4},
  pages         = {830--844},
  issn          = {2330-1643},
  __markedentry = {[felipe:1]},
  abstract      = {Tags (keywords freely assigned by users to describe web content) have become highly popular on Web 2.0 applications, because of the strong stimuli and easiness for users to create and describe their own content. This increase in tag popularity has led to a vast literature on tag recommendation methods. These methods aim at assisting users in the tagging process, possibly increasing the quality of the generated tags and, consequently, improving the quality of the information retrieval (IR) services that rely on tags as data sources. Regardless of the numerous and diversified previous studies on tag recommendation, to our knowledge, no previous work has summarized and organized them into a single survey article. In this article, we propose a taxonomy for tag recommendation methods, classifying them according to the target of the recommendations, their objectives, exploited data sources, and underlying techniques. Moreover, we provide a critical overview of these methods, pointing out their advantages and disadvantages. Finally, we describe the main open challenges related to the field, such as tag ambiguity, cold start, and evaluation issues.},
  doi           = {10.1002/asi.23736},
  keywords      = {rank3},
  url           = {http://dx.doi.org/10.1002/asi.23736},
}

@Article{wang_etal_2012c,
  author        = {Wang, Meng and Ni, Bingbing and Hua, Xian-Sheng and Chua, Tat-Seng},
  title         = {Assistive Tagging: A Survey of Multimedia Tagging with Human-computer Joint Exploration},
  journal       = {ACM Comput. Surv.},
  year          = {2012},
  volume        = {44},
  number        = {4},
  pages         = {25:1--25:24},
  month         = sep,
  issn          = {0360-0300},
  __markedentry = {[felipe:1]},
  acmid         = {2333120},
  address       = {New York, NY, USA},
  articleno     = {25},
  doi           = {10.1145/2333112.2333120},
  issue_date    = {August 2012},
  keywords      = {Annotation, interactive tagging, tag location, tag recommendation, tag refinement, tagging},
  numpages      = {24},
  publisher     = {ACM},
  url           = {http://doi.acm.org/10.1145/2333112.2333120},
}

@InBook{sammut_webb_2010,
  pages     = {137--138},
  title     = {Breakeven Point},
  publisher = {Springer US},
  year      = {2010},
  editor    = {Sammut, Claude and Webb, Geoffrey I.},
  address   = {Boston, MA},
  isbn      = {978-0-387-30164-8},
  booktitle = {Encyclopedia of Machine Learning},
  doi       = {10.1007/978-0-387-30164-8_88},
  url       = {https://doi.org/10.1007/978-0-387-30164-8_88},
}

@Article{farooq_etal_2007,
  author   = {U. Farooq and Y. Song and J. M. Carroll and C. L. Giles},
  title    = {Social Bookmarking for Scholarly Digital Libraries},
  journal  = {IEEE Internet Computing},
  year     = {2007},
  volume   = {11},
  number   = {6},
  pages    = {29-35},
  month    = {Nov},
  issn     = {1089-7801},
  abstract = {Social bookmarking services have recently gained popularity among Web users. Whereas numerous studies provide a historical account of tagging systems, the authors use their analysis of a domain-specific social bookmarking service called CiteULike to reflect on two metrics for evaluating tagging behavior: tag growth and tag reuse. They examine the relationship between these two metrics and articulate design implications for enhancing social bookmarking services. The authors briefly reflect on their own work developing a social bookmarking service for CiteSeer.},
  doi      = {10.1109/MIC.2007.135},
  keywords = {Internet;digital libraries;groupware;indexing;vocabulary;CiteSeer service;CiteULike service;Web user;domain-specific social bookmarking service;indexing system;online scientific collaboration;scholarly digital library;tag vocabulary;tagging system;Anatomy;Application software;Computer science;Data analysis;Recycling;Software libraries;Tagging;Terminology;Vocabulary;digital libraries;social bookmrking services;social media;social search;tag growth;tag reuse;tagging},
}

@InProceedings{illig_etal_2011,
  author        = {Illig, Jens and Hotho, Andreas and J\"{a}schke, Robert and Stumme, Gerd},
  title         = {A Comparison of Content-based Tag Recommendations in Folksonomy Systems},
  booktitle     = {Proceedings of the First International Conference on Knowledge Processing and Data Analysis},
  year          = {2011},
  series        = {KONT'07/KPP'07},
  pages         = {136--149},
  address       = {Berlin, Heidelberg},
  publisher     = {Springer-Verlag},
  __markedentry = {[felipe:1]},
  acmid         = {2022778},
  isbn          = {978-3-642-22139-2},
  location      = {Novosibirsk, Russia},
  numpages      = {14},
  url           = {http://dl.acm.org/citation.cfm?id=2022767.2022778},
}

@InProceedings{zubiaga_etal_2009,
  author        = {A. Zubiaga and A. P. García-Plaza and V. Fresno and R. Martínez},
  title         = {Content-Based Clustering for Tag Cloud Visualization},
  booktitle     = {2009 International Conference on Advances in Social Network Analysis and Mining},
  year          = {2009},
  pages         = {316-319},
  month         = {July},
  __markedentry = {[felipe:1]},
  abstract      = {Social tagging systems are becoming an interesting way to retrieve web information from previously annotated data. These sites present a tag cloud made up by the most popular tags, where neither tag grouping nor their corresponding content is considered. We present a methodology to obtain and visualize a cloud of related tags based on the use of self-organizing maps, and where the relations among tags are established taking into account the textual content of tagged documents. Each map unit can be represented by the most relevant terms of the tags it contains, so that it is possible to study and analyze the groups as well as to visualize and navigate through the relevant terms and tags.},
  doi           = {10.1109/ASONAM.2009.19},
  keywords      = {Internet;data visualisation;information retrieval;pattern clustering;self-organising feature maps;social networking (online);content-based clustering;self-organizing maps;social tagging;tag cloud visualisation;tagged documents;textual content;web information retrieval;Data visualization;Feeds;Navigation;Ontologies;Self organizing feature maps;Semantic Web;Social network services;Subscriptions;Tag clouds;Tagging;clustering;information access;social-tagging;visualization},
}

@Comment{jabref-meta: databaseType:bibtex;}

@InProceedings{kataria_agarwal_2015,
  author    = {S. Kataria and A. Agarwal},
  title     = {Distributed Representations for Content-Based and Personalized Tag Recommendation},
  booktitle = {2015 IEEE International Conference on Data Mining Workshop (ICDMW)},
  year      = {2015},
  pages     = {1388-1395},
  month     = {Nov},
  abstract  = {Summary form only given. Strong light-matter coupling has been recently successfully explored in the GHz and THz [1] range with on-chip platforms. New and intriguing quantum optical phenomena have been predicted in the ultrastrong coupling regime [2], when the coupling strength Ω becomes comparable to the unperturbed frequency of the system ω. We recently proposed a new experimental platform where we couple the inter-Landau level transition of an high-mobility 2DEG to the highly subwavelength photonic mode of an LC meta-atom [3] showing very large Ω/ωc = 0.87. Our system benefits from the collective enhancement of the light-matter coupling which comes from the scaling of the coupling Ω ∝ √n, were n is the number of optically active electrons. In our previous experiments [3] and in literature [4] this number varies from 104-103 electrons per meta-atom. We now engineer a new cavity, resonant at 290 GHz, with an extremely reduced effective mode surface Seff = 4 × 10-14 m2 (FE simulations, CST), yielding large field enhancements above 1500 and allowing to enter the few (<;100) electron regime. It consist of a complementary metasurface with two very sharp metallic tips separated by a 60 nm gap (Fig.1(a, b)) on top of a single triangular quantum well. THz-TDS transmission experiments as a function of the applied magnetic field reveal strong anticrossing of the cavity mode with linear cyclotron dispersion. Measurements for arrays of only 12 cavities are reported in Fig.1(c). On the top horizontal axis we report the number of electrons occupying the topmost Landau level as a function of the magnetic field. At the anticrossing field of B=0.73 T we measure approximately 60 electrons ultra strongly coupled (Ω/ω- ||},
  doi       = {10.1109/ICDMW.2015.240},
  keywords  = {indexing;learning (artificial intelligence);natural language processing;neural nets;recommender systems;search problems;book-marking datasets;content-based recommendation;corpus wide topic-level information;distributed representation learning problem;document level word sequence information;jointly trained vector representations;low-dimensional vectors;multilayered neural language model;personalized tag recommendation tasks;two stage method;Conferences;Context;Context modeling;Optimization;Semantics;Tagging;Training},
}

@article{peralta_2007,
author = {Peralta, Veronika},
year = {2007},
month = {08},
pages = {},
title = {Extraction and Integration of MovieLens and IMDb Data}
}

@article{obar_wildman_2015,
title = "Social media definition and the governance challenge: An introduction to the special issue",
journal = "Telecommunications Policy",
volume = "39",
number = "9",
pages = "745 - 750",
year = "2015",
note = "SPECIAL ISSUE ON THE GOVERNANCE OF SOCIAL MEDIA",
issn = "0308-5961",
doi = "https://doi.org/10.1016/j.telpol.2015.07.014",
url = "http://www.sciencedirect.com/science/article/pii/S0308596115001172",
author = "Jonathan A. Obar and Steve Wildman"
}

@inbook{hamburger_etal_2017,
title = {Social Networking},
author = {Amichai-Hamburger, Yair and Hayat, Tsahi},
author = {},
publisher = {John Wiley \& Sons, Inc.},
isbn = {9781118783764},
url = {http://dx.doi.org/10.1002/9781118783764.wbieme0170},
doi = {10.1002/9781118783764.wbieme0170},
keywords = {computer-mediated communication, digital culture, social networks},
booktitle = {The International Encyclopedia of Media Effects},
year = {2017},
}

@article{aiello_2012,
 author = {Aiello, Luca Maria and Barrat, Alain and Schifanella, Rossano and Cattuto, Ciro and Markines, Benjamin and Menczer, Filippo},
 title = {Friendship Prediction and Homophily in Social Media},
 journal = {ACM Trans. Web},
 issue_date = {May 2012},
 volume = {6},
 number = {2},
 month = jun,
 year = {2012},
 issn = {1559-1131},
 pages = {9:1--9:33},
 articleno = {9},
 numpages = {33},
 url = {http://doi.acm.org/10.1145/2180861.2180866},
 doi = {10.1145/2180861.2180866},
 acmid = {2180866},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {Social media, collaborative tagging, folksonomies, homophily, link prediction, maximum Information path, social network, topical similarity},
} 

@inproceedings{helic_etal_2012,
 author = {Helic, Denis and K\"{o}rner, Christian and Granitzer, Michael and Strohmaier, Markus and Trattner, Christoph},
 title = {Navigational Efficiency of Broad vs. Narrow Folksonomies},
 booktitle = {Proceedings of the 23rd ACM Conference on Hypertext and Social Media},
 series = {HT '12},
 year = {2012},
 isbn = {978-1-4503-1335-3},
 location = {Milwaukee, Wisconsin, USA},
 pages = {63--72},
 numpages = {10},
 url = {http://doi.acm.org/10.1145/2309996.2310008},
 doi = {10.1145/2309996.2310008},
 acmid = {2310008},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {folksonomy, keywords, navigation, tags},
} 

@book{peters_2009,
 author = {Peters, Isabella},
 title = {Folksonomies. Indexing and Retrieval in Web 2.0},
 year = {2009},
 isbn = {3598251793, 9783598251795},
 edition = {1st},
 publisher = {Walter de Gruyter \& Co.},
 address = {Hawthorne, NJ, USA},
} 

@InCollection{baumeister_leary_1997,
  author    = {Baumeister, Roy F. and Leary, Mark R.},
  title     = {Writing narrative literature reviews},
  booktitle = {Review of General Psychology},
  year      = {1997},
  url       = {http://psycnet.apa.org/doiLanding?doi=10.1037\%2F1089-2680.1.3.311},
}

@ARTICLE{bem_1995,
    author = {Daryl J. Bem},
    title = {Writing a Review Article for Psychological Bulletin},
    journal = {Psychological Bulletin},
    year = {1995},
    pages = {172--177}
}

@inproceedings{sigurnbjornsson_van_zwol_2008,
 author = {Sigurbj\"{o}rnsson, B\"{o}rkur and van Zwol, Roelof},
 title = {Flickr Tag Recommendation Based on Collective Knowledge},
 booktitle = {Proceedings of the 17th International Conference on World Wide Web},
 series = {WWW '08},
 year = {2008},
 isbn = {978-1-60558-085-2},
 location = {Beijing, China},
 pages = {327--336},
 numpages = {10},
 url = {http://doi-acm-org.ez29.capes.proxy.ufrj.br/10.1145/1367497.1367542},
 doi = {10.1145/1367497.1367542},
 acmid = {1367542},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {aggregated tag suggestion, collective knowledge, flickr, photo annotations, tag characterisation, tag co-occurence, tag recommendation},
} 

@article{blei_etal_2003,
 author = {Blei, David M. and Ng, Andrew Y. and Jordan, Michael I.},
 title = {Latent Dirichlet Allocation},
 journal = {J. Mach. Learn. Res.},
 issue_date = {3/1/2003},
 volume = {3},
 month = mar,
 year = {2003},
 issn = {1532-4435},
 pages = {993--1022},
 numpages = {30},
 url = {http://dl.acm.org/citation.cfm?id=944919.944937},
 acmid = {944937},
 publisher = {JMLR.org},
} 

@article{kullback_leibler_1951,
author = "Kullback, S. and Leibler, R. A.",
doi = "10.1214/aoms/1177729694",
fjournal = "The Annals of Mathematical Statistics",
journal = "Ann. Math. Statist.",
month = "03",
number = "1",
pages = "79--86",
publisher = "The Institute of Mathematical Statistics",
title = "On Information and Sufficiency",
url = "https://doi.org/10.1214/aoms/1177729694",
volume = "22",
year = "1951"
}

@article{dietterich_etal_1997,
 author = {Dietterich, Thomas G. and Lathrop, Richard H. and Lozano-P{\'e}rez, Tom\'{a}s},
 title = {Solving the Multiple Instance Problem with Axis-parallel Rectangles},
 journal = {Artif. Intell.},
 issue_date = {Jan. 1997},
 volume = {89},
 number = {1-2},
 month = jan,
 year = {1997},
 issn = {0004-3702},
 pages = {31--71},
 numpages = {41},
 url = {http://dx.doi.org/10.1016/S0004-3702(96)00034-3},
 doi = {10.1016/S0004-3702(96)00034-3},
 acmid = {249682},
 publisher = {Elsevier Science Publishers Ltd.},
 address = {Essex, UK},
 keywords = {drug design, machine learning, structure-activity relationships},
} 

@inproceedings{hearst_1994,
 author = {Hearst, Marti A.},
 title = {Multi-paragraph Segmentation of Expository Text},
 booktitle = {Proceedings of the 32Nd Annual Meeting on Association for Computational Linguistics},
 series = {ACL '94},
 year = {1994},
 location = {Las Cruces, New Mexico},
 pages = {9--16},
 numpages = {8},
 url = {https://doi.org/10.3115/981732.981734},
 doi = {10.3115/981732.981734},
 acmid = {981734},
 publisher = {Association for Computational Linguistics},
 address = {Stroudsburg, PA, USA},
} 

@Book{edgar_2008,
author={Edgar,Gerald},
title = {Measure, Topology, and Fractal Geometry},
booktitle = {Measure, Topology, and Fractal Geometry},
year={2008},
publisher={Springer-Verlag New York}

}

@article{sokolova_and_napalme_2009,
title = "A systematic analysis of performance measures for classification tasks",
journal = "Information Processing \& Management",
volume = "45",
number = "4",
pages = "427 - 437",
year = "2009",
issn = "0306-4573",
doi = "https://doi.org/10.1016/j.ipm.2009.03.002",
url = "http://www.sciencedirect.com/science/article/pii/S0306457309000259",
author = "Marina Sokolova and Guy Lapalme",
keywords = "Performance evaluation, Machine Learning, Text classification"
}

@MISC{hsu_etal_2010,
    author = {Chih-wei Hsu and Chih-chung Chang and Chih-jen Lin},
    title = {A practical guide to support vector classification },
    year = {2010}
}

@inproceedings{le_mikolov_2014,
 author = {Le, Quoc and Mikolov, Tomas},
 title = {Distributed Representations of Sentences and Documents},
 booktitle = {Proceedings of the 31st International Conference on International Conference on Machine Learning - Volume 32},
 series = {ICML'14},
 year = {2014},
 location = {Beijing, China},
 pages = {II-1188--II-1196},
 url = {http://dl.acm.org/citation.cfm?id=3044805.3045025},
 acmid = {3045025},
 publisher = {JMLR.org},
} 

@inproceedings{rehurek_2010,
      title = {{Software Framework for Topic Modelling with Large Corpora}},
      author = {Radim {\v R}eh{\r u}{\v r}ek and Petr Sojka},
      booktitle = {{Proceedings of the LREC 2010 Workshop on New
           Challenges for NLP Frameworks}},
      pages = {45--50},
      year = 2010,
      month = May,
      day = 22,
      publisher = {ELRA},
      address = {Valletta, Malta},
      note={\url{http://is.muni.cz/publication/884893/en}},
      language={English}
}

@article{correa_etal_2017,
  author    = {Edilson Anselmo Corr{\^{e}}a J{\'{u}}nior and
               Vanessa Queiroz Marinho and
               Leandro Borges dos Santos},
  title     = {{NILC-USP} at SemEval-2017 Task 4: {A} Multi-view Ensemble for Twitter
               Sentiment Analysis},
  journal   = {CoRR},
  volume    = {abs/1704.02263},
  year      = {2017},
  url       = {http://arxiv.org/abs/1704.02263},
  archivePrefix = {arXiv},
  eprint    = {1704.02263},
  timestamp = {Wed, 25 Oct 2017 16:39:35 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/CorreaMS17},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@InProceedings{xhao_etal_2015,
  author    = {Zhao, Jiang  and  Lan, Man},
  title     = {ECNU: Leveraging Word Embeddings to Boost Performance for Paraphrase in Twitter},
  booktitle = {Proceedings of the 9th International Workshop on Semantic Evaluation (SemEval 2015)},
  month     = {June},
  year      = {2015},
  address   = {Denver, Colorado},
  publisher = {Association for Computational Linguistics},
  pages     = {34--39},
  url       = {http://www.aclweb.org/anthology/S15-2006}
}

@inproceedings{li_etal_2015,
  title={Data Sparseness in Linear SVM},
  author={Xiang Li and Huaimin Wang and Bin Gu and Charles X. Ling},
  booktitle={IJCAI},
  year={2015}
}

@ARTICLE{tsoumakas_katakis_2007,
    author = {Grigorios Tsoumakas and Ioannis Katakis},
    title = {Multi-label classification: An overview},
    journal = {Int J Data Warehousing and Mining},
    year = {2007},
    volume = {2007},
    pages = {1--13}
}

@article{rifkin_klautau_2004,
 author = {Rifkin, Ryan and Klautau, Aldebaro},
 title = {In Defense of One-Vs-All Classification},
 journal = {J. Mach. Learn. Res.},
 issue_date = {12/1/2004},
 volume = {5},
 month = dec,
 year = {2004},
 issn = {1532-4435},
 pages = {101--141},
 numpages = {41},
 url = {http://dl.acm.org/citation.cfm?id=1005332.1005336},
 acmid = {1005336},
 publisher = {JMLR.org},
} 

@article{seo_etal_2003,
  title={Soft nearest prototype classification},
  author={Seo, Sambu and Bode, Mathias and Obermayer, Klaus},
  journal={IEEE Transactions on Neural Networks},
  volume={14},
  number={2},
  pages={390--398},
  year={2003},
  publisher={IEEE}
}

@incollection{zhou_zhang_2006,
title = {Multi-Instance Multi-Label Learning with Application to Scene Classification},
author = {Zhang, Min-Ling and Zhou, Zhi-Hua},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {B. Sch\"{o}lkopf and J. C. Platt and T. Hoffman},
pages = {1609--1616},
year = {2006},
publisher = {MIT Press},
url = {http://papers.nips.cc/paper/3047-multi-instance-multi-label-learning-with-application-to-scene-classification.pdf}
}


@misc{kaufmanl_rousseeuw_1987,
  added-at = {2017-07-03T12:14:53.000+0200},
  author = {Kaufman, Leonard and Rousseeuw, Peter J.},
  biburl = {https://www.bibsonomy.org/bibtex/22b132bef56453004f6326777af4ee791/nosebrain},
  editor = {Y, In: Dodge and editor},
  interhash = {ef930c034e33409e95869d0016cc74a0},
  intrahash = {2b132bef56453004f6326777af4ee791},
  keywords = {clustering kmediods lecture:data-mining pam},
  location = {Amsterdam:},
  pages = {405–416},
  publisher = {North Holland / Elsevier},
  timestamp = {2017-08-03T17:03:26.000+0200},
  title = {Clustering by means of medoids},
  year = 1987
}

@article{huttenlocher_etal_1993,
 author = {Huttenlocher, D. P. and Klanderman, G. A. and Rucklidge, W. A.},
 title = {Comparing Images Using the Hausdorff Distance},
 journal = {IEEE Trans. Pattern Anal. Mach. Intell.},
 issue_date = {September 1993},
 volume = {15},
 number = {9},
 month = sep,
 year = {1993},
 issn = {0162-8828},
 pages = {850--863},
 numpages = {14},
 url = {https://doi.org/10.1109/34.232073},
 doi = {10.1109/34.232073},
 acmid = {628513},
 publisher = {IEEE Computer Society},
 address = {Washington, DC, USA},
 keywords = {Hausdorff distance, binary image, image comparison, image processing, position error tolerance, rigid motion, shape comparison methods, translation},
} 

@article{zhang_zhou_2007,
 author = {Zhang, Min-Ling and Zhou, Zhi-Hua},
 title = {ML-KNN: A Lazy Learning Approach to Multi-label Learning},
 journal = {Pattern Recogn.},
 issue_date = {July, 2007},
 volume = {40},
 number = {7},
 month = jul,
 year = {2007},
 issn = {0031-3203},
 pages = {2038--2048},
 numpages = {11},
 url = {http://dx.doi.org/10.1016/j.patcog.2006.12.019},
 doi = {10.1016/j.patcog.2006.12.019},
 acmid = {1234635},
 publisher = {Elsevier Science Inc.},
 address = {New York, NY, USA},
 keywords = {Functional genomics, K-nearest neighbor, KNN, K-nearest neighbor, Lazy learning, MAP, maximum a posteriori, ML-KNN, multi-label K-nearest neighbor, Machine learning, Multi-label learning, Natural scene classification, PMM, parametric mixture model, Text categorization},
} 

@inproceedings{mikolov_etal_2013,
  author    = {Tomas Mikolov and
               Ilya Sutskever and
               Kai Chen and
               Gregory S. Corrado and
               Jeffrey Dean},
  title     = {Distributed Representations of Words and Phrases and their Compositionality},
  booktitle = {Advances in Neural Information Processing Systems 26: 27th Annual
               Conference on Neural Information Processing Systems 2013. Proceedings
               of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States.},
  pages     = {3111--3119},
  year      = {2013},
  crossref  = {DBLP:conf/nips/2013},
  url       = {http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality},
  timestamp = {Thu, 07 May 2015 20:02:01 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nips/MikolovSCCD13},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@INPROCEEDINGS{arthur_vassilvitskii_2007,
    author = {David Arthur and Sergei Vassilvitskii},
    title = {K-means++: the advantages of careful seeding},
    booktitle = {In Proceedings of the 18th Annual ACM-SIAM Symposium on Discrete Algorithms},
    year = {2007}
}

@INPROCEEDINGS{macqueen_1967,
    author = {J. Macqueen},
    title = {Some methods for classification and analysis of multivariate observations},
    booktitle = {In 5-th Berkeley Symposium on Mathematical Statistics and Probability},
    year = {1967},
    pages = {281--297}
}

@book{jain_dubes_1988,
 author = {Jain, Anil K. and Dubes, Richard C.},
 title = {Algorithms for Clustering Data},
 year = {1988},
 isbn = {0-13-022278-X},
 publisher = {Prentice-Hall, Inc.},
 address = {Upper Saddle River, NJ, USA},
} 
@article{wieting_etal_2016,
  author    = {John Wieting and
               Mohit Bansal and
               Kevin Gimpel and
               Karen Livescu},
  title     = {Towards Universal Paraphrastic Sentence Embeddings},
  journal   = {CoRR},
  volume    = {abs/1511.08198},
  year      = {2015},
  url       = {http://arxiv.org/abs/1511.08198},
  archivePrefix = {arXiv},
  eprint    = {1511.08198},
  timestamp = {Wed, 07 Jun 2017 14:40:28 +0200},
  biburl    = {https://dblp.org/rec/bib/journals/corr/WietingBGL15a},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@article{arora_etal_2017, 
	author = {Sanjeev Arora and Yingyu Liang and Tengyu Ma}, 
	title = {A Simple but Tough-to-Beat Baseline for Sentence Embeddings}, 
	booktitle = {International Conference on Learning Representations},
	year = {2017}
}

@INPROCEEDINGS{maron_ratan_98,
    author = {Oded Maron and Aparna Lakshmi Ratan},
    title = {Multiple-Instance Learning for Natural Scene Classification},
    booktitle = {In The Fifteenth International Conference on Machine Learning},
    year = {1998},
    pages = {341--349},
    publisher = {Morgan Kaufmann}
}

@INPROCEEDINGS{andrews_etal_2003,
    author = {Stuart Andrews and Ioannis Tsochantaridis and Thomas Hofmann},
    title = {Support vector machines for multiple-instance learning},
    booktitle = {Advances in Neural Information Processing Systems 15},
    year = {2003},
    pages = {561--568},
    publisher = {MIT Press}
}

@book{marinho_etal_2012,
 author = {Marinho, Leandro Balby and Hotho, Andreas and Jschke, Robert and Nanopoulos, Alexandros and Rendle, Steffen and Schmidt-Thieme, Lars and Stumme, Gerd and Symeonidis, Panagiotis},
 title = {Recommender Systems for Social Tagging Systems},
 year = {2012},
 isbn = {1461418933, 9781461418931},
 publisher = {Springer Publishing Company, Incorporated},
} 

@ARTICLE{deerwester_etal_1990,
    author = {Scott Deerwester and Susan T. Dumais and George W. Furnas and Thomas K. Landauer and Richard Harshman},
    title = {Indexing by latent semantic analysis},
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}


@ARTICLE{gillis_2014,
   author = {{Gillis}, N.},
    title = "{The Why and How of Nonnegative Matrix Factorization}",
  journal = {ArXiv e-prints},
archivePrefix = "arXiv",
   eprint = {1401.5226},
 primaryClass = "stat.ML",
 keywords = {Statistics - Machine Learning, Computer Science - Information Retrieval, Computer Science - Learning, Mathematics - Optimization and Control},
     year = 2014,
    month = jan,
   adsurl = {http://adsabs.harvard.edu/abs/2014arXiv1401.5226G},
  adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

@article{huang_etal_2011,
author = {Huang, Jianbin and Sun, Heli and Han, Jiawei and Feng, Boqin},
year = {2011},
month = {06},
pages = {2160-2171},
title = {Density-based shrinkage for revealing hierarchical and overlapping community structure in networks},
volume = {390},
booktitle = {Physica A-statistical Mechanics and Its Applications - PHYSICA A}
}

@incollection{rabiner_1989,
 author = {Rabiner, Lawrence R.},
 chapter = {A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition},
 title = {Readings in Speech Recognition},
 editor = {Waibel, Alex and Lee, Kai-Fu},
 year = {1990},
 isbn = {1-55860-124-4},
 pages = {267--296},
 numpages = {30},
 url = {http://dl.acm.org/citation.cfm?id=108235.108253},
 acmid = {108253},
 publisher = {Morgan Kaufmann Publishers Inc.},
 address = {San Francisco, CA, USA},
} 

@Article{rawashdeh_etal_2012,
author="Rawashdeh, Majdi
and Kim, Heung-Nam
and Alja'am, Jihad Mohamad
and El Saddik, Abdulmotaleb",
title="Folksonomy link prediction based on a tripartite graph for tag recommendation",
journal="Journal of Intelligent Information Systems",
year="2013",
month="Apr",
day="01",
volume="40",
number="2",
pages="307--325",
abstract="Nowadays social tagging has become a popular way to annotate, search, navigate and discover online resources, in turn leading to the sheer amount of user-generated metadata. This paper addresses the problem of recommending suitable tags during folksonomy development from a graph-based perspective. The proposed approach adapts the Katz measure, a path-ensemble based proximity measure, for the use in social tagging systems. We model a folksonomy as a weighted, undirected tripartite graph. We then apply the Katz measure to this graph, and exploit it to provide tag recommendations for individual users. We evaluate our method on two real-world folksonomies collected from CiteULike and Last.fm. The experimental results demonstrate that the proposed method improves the recommendation performance and is effective for both active taggers and cold-start taggers compared to existing algorithms.",
issn="1573-7675",
doi="10.1007/s10844-012-0227-2",
url="https://doi.org/10.1007/s10844-012-0227-2"
}

@book{berge_1985,
 author = {Berge, C.},
 title = {Graphs and Hypergraphs},
 year = {1985},
 isbn = {0720404797},
 publisher = {Elsevier Science Ltd.},
 address = {Oxford, UK, UK},
} 

@article{salton_1975,
 author = {Salton, G. and Wong, A. and Yang, C. S.},
 title = {A Vector Space Model for Automatic Indexing},
 journal = {Commun. ACM},
 issue_date = {Nov. 1975},
 volume = {18},
 number = {11},
 month = nov,
 year = {1975},
 issn = {0001-0782},
 pages = {613--620},
 numpages = {8},
 url = {http://doi.acm.org/10.1145/361219.361220},
 doi = {10.1145/361219.361220},
 acmid = {361220},
 publisher = {ACM},
 address = {New York, NY, USA},
 keywords = {automatic indexing, automatic information retrieval, content analysis, document space},
} 

@inproceedings{linguisticregularities,
  author    = {Tomas Mikolov and
               Wen{-}tau Yih and
               Geoffrey Zweig},
  title     = {Linguistic Regularities in Continuous Space Word Representations},
  booktitle = {Human Language Technologies: Conference of the North American Chapter
               of the Association of Computational Linguistics, Proceedings, June
               9-14, 2013, Westin Peachtree Plaza Hotel, Atlanta, Georgia, {USA}},
  pages     = {746--751},
  year      = {2013a},
  crossref  = {DBLP:conf/naacl/2013},
  url       = {http://aclweb.org/anthology/N/N13/N13-1090.pdf},
  timestamp = {Fri, 21 Feb 2014 14:03:41 +0100},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/naacl/MikolovYZ13},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@article{efficientestimation,
  author    = {Tomas Mikolov and
               Kai Chen and
               Greg Corrado and
               Jeffrey Dean},
  title     = {Efficient Estimation of Word Representations in Vector Space},
  journal   = {CoRR},
  volume    = {abs/1301.3781},
  year      = {2013b},
  url       = {http://arxiv.org/abs/1301.3781},
  timestamp = {Thu, 07 May 2015 20:02:01 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/journals/corr/abs-1301-3781},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}

@inproceedings{distreprofwords,
  author    = {Tomas Mikolov and
               Ilya Sutskever and
               Kai Chen and
               Gregory S. Corrado and
               Jeffrey Dean},
  title     = {Distributed Representations of Words and Phrases and their Compositionality},
  booktitle = {Advances in Neural Information Processing Systems 26: 27th Annual
               Conference on Neural Information Processing Systems 2013. Proceedings
               of a meeting held December 5-8, 2013, Lake Tahoe, Nevada, United States.},
  pages     = {3111--3119},
  year      = {2013c},
  crossref  = {DBLP:conf/nips/2013},
  url       = {http://papers.nips.cc/paper/5021-distributed-representations-of-words-and-phrases-and-their-compositionality},
  timestamp = {Thu, 07 May 2015 20:02:01 +0200},
  biburl    = {http://dblp.uni-trier.de/rec/bib/conf/nips/MikolovSCCD13},
  bibsource = {dblp computer science bibliography, http://dblp.org}
}
@inproceedings{turian_et_al_2010,
 author = {Turian, Joseph and Ratinov, Lev and Bengio, Yoshua},
 title = {Word Representations: A Simple and General Method for Semi-supervised Learning},
 booktitle = {Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics},
 series = {ACL '10},
 year = {2010},
 location = {Uppsala, Sweden},
 pages = {384--394},
 numpages = {11},
 url = {http://dl.acm.org/citation.cfm?id=1858681.1858721},
 acmid = {1858721},
 publisher = {Association for Computational Linguistics},
 address = {Stroudsburg, PA, USA},
} 

@MISC{maaten_etal_2008,
    author = {L.J.P. van der Maaten and E. O. Postma and H. J. van den Herik},
    title = { Dimensionality Reduction: A Comparative Review},
    year = {2008}
}

@article{wold_etal_1987,
title = "Principal component analysis",
journal = "Chemometrics and Intelligent Laboratory Systems",
volume = "2",
number = "1",
pages = "37 - 52",
year = "1987",
note = "Proceedings of the Multivariate Statistical Workshop for Geologists and Geochemists",
issn = "0169-7439",
doi = "https://doi.org/10.1016/0169-7439(87)80084-9",
url = "http://www.sciencedirect.com/science/article/pii/0169743987800849",
author = "Svante Wold and Kim Esbensen and Paul Geladi"
}

@incollection{bengio_etal_2007,
title = {Greedy Layer-Wise Training of Deep Networks},
author = {Bengio, Yoshua and Pascal Lamblin and Dan Popovici and Larochelle, Hugo},
booktitle = {Advances in Neural Information Processing Systems 19},
editor = {B. Sch\"{o}lkopf and J. C. Platt and T. Hoffman},
pages = {153--160},
year = {2007},
publisher = {MIT Press},
url = {http://papers.nips.cc/paper/3048-greedy-layer-wise-training-of-deep-networks.pdf}
}

@article{jain_2010,
title = "Data clustering: 50 years beyond K-means",
journal = "Pattern Recognition Letters",
volume = "31",
number = "8",
pages = "651 - 666",
year = "2010",
note = "Award winning papers from the 19th International Conference on Pattern Recognition (ICPR)",
issn = "0167-8655",
doi = "https://doi.org/10.1016/j.patrec.2009.09.011",
url = "http://www.sciencedirect.com/science/article/pii/S0167865509002323",
author = "Anil K. Jain",
keywords = "Data clustering, User’s dilemma, Historical developments, Perspectives on clustering, King-Sun Fu prize"
}

@article{pan_yang_2010,
 author = {Pan, Sinno Jialin and Yang, Qiang},
 title = {A Survey on Transfer Learning},
 journal = {IEEE Trans. on Knowl. and Data Eng.},
 issue_date = {October 2010},
 volume = {22},
 number = {10},
 month = oct,
 year = {2010},
 issn = {1041-4347},
 pages = {1345--1359},
 numpages = {15},
 url = {http://dx.doi.org/10.1109/TKDE.2009.191},
 doi = {10.1109/TKDE.2009.191},
 acmid = {1850545},
 publisher = {IEEE Educational Activities Department},
 address = {Piscataway, NJ, USA},
 keywords = {Transfer learning, Transfer learning, survey, machine learning, data mining., data mining., machine learning, survey},
} 

@incollection{bordes_etal_2013,
title = {Translating Embeddings for Modeling Multi-relational Data},
author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Duran, Alberto and Weston, Jason and Yakhnenko, Oksana},
booktitle = {Advances in Neural Information Processing Systems 26},
editor = {C. J. C. Burges and L. Bottou and M. Welling and Z. Ghahramani and K. Q. Weinberger},
pages = {2787--2795},
year = {2013},
publisher = {Curran Associates, Inc.},
url = {http://papers.nips.cc/paper/5071-translating-embeddings-for-modeling-multi-relational-data.pdf}
}

@article{haralick_shapiro_1985,
title = "Image segmentation techniques",
journal = "Computer Vision, Graphics, and Image Processing",
volume = "29",
number = "1",
pages = "100 - 132",
year = "1985",
issn = "0734-189X",
doi = "https://doi.org/10.1016/S0734-189X(85)90153-7",
url = "http://www.sciencedirect.com/science/article/pii/S0734189X85901537",
author = "Robert M. Haralick and Linda G. Shapiro"
}

@techreport{page_etal_1999,
          number = {1999-66},
           month = {November},
          author = {Lawrence Page and Sergey Brin and Rajeev Motwani and Terry Winograd},
            note = {Previous number = SIDL-WP-1999-0120},
           title = {The PageRank Citation Ranking: Bringing Order to the Web.},
            type = {Technical Report},
       publisher = {Stanford InfoLab},
            year = {1999},
     institution = {Stanford InfoLab},
             url = {http://ilpubs.stanford.edu:8090/422/},
        abstract = {The importance of a Web page is an inherently subjective matter, which depends on the readers interests, knowledge and attitudes. But there is still much that can be said objectively about the relative importance of Web pages. This paper describes PageRank, a mathod for rating Web pages objectively and mechanically, effectively measuring the human interest and attention devoted to them. We compare PageRank to an idealized random Web surfer. We show how to efficiently compute PageRank for large numbers of pages. And, we show how to apply PageRank to search and to user navigation.}
}

@Inbook{tsoumakas_2010,
author="Tsoumakas, Grigorios
and Katakis, Ioannis
and Vlahavas, Ioannis",
editor="Maimon, Oded
and Rokach, Lior",
title="Mining Multi-label Data",
bookTitle="Data Mining and Knowledge Discovery Handbook",
year="2010",
publisher="Springer US",
address="Boston, MA",
pages="667--685",
abstract="A large body of research in supervised learning deals with the analysis of single-label data, where training examples are associated with a single label $\lambda$ from a set of disjoint labels L. However, training examples in several application domains are often associated with a set of labels Y ⊆ L. Such data are called multi-label.",
isbn="978-0-387-09823-4",
doi="10.1007/978-0-387-09823-4_34",
url="https://doi.org/10.1007/978-0-387-09823-4_34"
}