- Leave a star if you find this useful
- Pull request if you want to contribute to this resource
| Name | Scene | Tasks | Information | URL |
|---|---|---|---|---|
| Amazon Review | Commerce | Seq Rec/CF Rec | This is a large crawl of product reviews from Amazon. Ratings: 82.83 million, Users: 20.98 million, Items: 9.35 million, Timespan: May 1996 - July 2014 | link |
| Amazon-M2 | Commerce | Seq Rec/CF Rec | A large dataset of anonymized user sessions with their interacted products collected from multiple language sources at Amazon. It includes 3,606,249 train sessions, 361,659 test sessions, and 1,410,675 products. | link link-2 |
| Steam | Game | Seq Rec/CF Rec | Reviews represent a great opportunity to break down the satisfaction and dissatisfaction factors around games. Reviews: 7,793,069, Users: 2,567,538, Items: 15,474, Bundles: 615 | link |
| MovieLens | Movie | General | The dataset consists of 4 sub-datasets, which describe users' ratings to movies and free-text tagging activities from MovieLens, a movie recommendation service. | link |
| Yelp | Commerce | General | There are 6,990,280 reviews, 150,346 businesses, 200,100 pictures, 11 metropolitan areas, 908,915 tips by 1,987,897 users. Over 1.2 million business attributes like hours, parking, availability, etc. | link |
| Douban | Movie, Music, Book | Seq Rec/CF Rec | This dataset includes three domains, i.e., movie, music, and book, and different kinds of raw information, i.e., ratings, reviews, item details, user profiles, tags (labels), and date. | link |
| MIND | News | General | MIND contains about 160k English news articles and more than 15 million impression logs generated by 1 million users. Every news contains textual content including title, abstract, body, category, and entities. | link |
| U-NEED | Commerce | Conversation Rec | U-NEED consists of 7,698 fine-grained annotated pre-sales dialogues, 333,879 user behaviors, and 332,148 product knowledge tuples. | link |
| PixelRec | Short Video | Seq Rec/CF Rec | PixelRec is a large dataset of cover images collected from a short video recommender system, comprising approximately 200 million user image interactions, 30 million users, and 400,000 video cover images. The texts and other aggregated attributes of videos are also included. | link |
| KuaiSAR | Video | Search and Rec | KuaiSAR contains genuine search and recommendation behaviors of 25,877 users, 6,890,707 items, 453,667 queries, and 19,664,885 actions within a span of 19 days on the Kuaishou app | link |
| Tenrec | Video, Article | General | Tenrec is a large-scale benchmark dataset for recommendation systems. It contains around 5 million users and 140 million interactions. | link |
This link contains all the datsets regarding RecSys - link
| Name | Information |
|---|---|
| OpenCv | Tutorials on OpenCv |
| Name | Tasks | Information | URL |
|---|---|---|---|
| DeepCarsKit | Context Aware | A deep learning based context-aware recommendation library | link |
| MM-Rec | MultiModal RecSys | MMRec is a MultiModal Recommendation toolbox based on PyTorch. It integrates more than ten outstanding multimodal recommendation system models | link |
| Cornac | MultiModal RecSys | Cornac is a comparative framework for multimodal recommender systems. It focuses on making it convenient to work with models leveraging auxiliary data (e.g., item descriptive text and image, social network, etc), Cornac enables fast experiments and straightforward implementations of new models. It is highly compatible with existing machine learning libraries (e.g., TensorFlow, PyTorch). |
Paper |
| Name | Information |
|---|---|
| Young Feng | Researcher - Notes/ Slides/ Projects on RecSys |
| PhD Thesis | A Compilation of RecSys thessis |
| Tensorflow Summit | Tensorflow Summit on RecSys 2023 - Their Tools explanation for RecSys |
| RecSys WorkShop | All the workshops of the RecSys |
| Article | Article on EEG with Music- From the Imotion |
| Tutorial | RecSys Tutorials from USA University, Young Feng |
| OpenSource AI Book | covering all the most important categories in the Open Source AI space, from model evaluations to deployment for ML/DL/LLM |
| Name | Information | Paper Link |
|---|---|---|
| Graham Jesnon | A list of the SOTA RecSys being used in the Industry and also links for the Open Source RecSys | N.A |
| Foundation Models for Recommender Systems: A Survey and New Perspectives | All the literature they have used, compiled over here | link |
| A Survey on Large Language Models for Recommendation | All the literature they have used, compiled over here | link |
| MultiModal RecSys | All the literature encompising the MultiModal RecSys | N.A |
| Name | Information |
|---|---|
| Deep learning reveals what vocal bursts express in different cultures | The models were trained on human intensity ratings of large-scale, experimentally controlled emotional expression data gathered using the methods described in these papers |
| Deep learning reveals what facial expressions mean to people in different cultures | The models were trained on human intensity ratings of large-scale, experimentally controlled emotional expression data gathered using the methods described in these papers |
https://wenqifan03.github.io/openings.html
| Name | Information |
|---|---|
| Web Application to Recommend Songs Based on Human Facial Expressions and Emotions | Can look at how he is recommending songs from spotify API |
Some open-source and effective projects can be adapted to the recommendation systems based on Chinese textual data. Especially for the individual researchers !
| Project | Year |
|---|---|
| Qwen1.5-7B | 2023 |
| baichuan-7B | 2023 |
| YuLan-chat | 2023 |
| Chinese-LLaMA-Alpaca | 2023 |
| THUDM/ChatGLM-6B | 2023 |
| FreedomIntelligence/LLMZoo Phoenix | 2023 |
| bloomz-7b1 | 2023 |
| LianjiaTech/BELLE | 2023 |
https://book.premai.io/state-of-open-source-ai/ Hope my Resources can help your work.