Code and dataset for CVPR 2019 paper "Learning Binary Code for Personalized Fashion Recommendation"
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Updated
Sep 2, 2019 - Python
Code and dataset for CVPR 2019 paper "Learning Binary Code for Personalized Fashion Recommendation"
personalized recommendation
Neural Graph Collaborative Filtering, SIGIR2019
Developed a hybrid-filtering personalized news articles recommendation system which can suggest articles from popular news service providers based on reading history of twitter users who share similar interests (Collaborative filtering) and content similarity of the article and user’s tweets (Content-based filtering)
Disentagnled Graph Collaborative Filtering, SIGIR2020
Ten Retail Data Analytics Projects, Heavy Focus on ML. Emphasis on consumer behavior.
A demo app to show how the implementation results look like when AWS Personalize is trained with movie lens dataset.
Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library
Movie Recommendation Anytime Anywhere
Personalized Visual Art Recommendation by Learning Latent Semantic Representations
It describes the features of AWS personalize.
An intuitive movie recommendation system leveraging genre similarity with TF-IDF and cosine similarity for a personalized film discovery experience.
Analyzing Temporal, Spatial, and Historical Data in Rating Prediction Algorithms: A Comparative Study
Unleash the Power of Music with Personalized Concert Recommendations.
A book search engine with support for title search, author search, and multilingual query and result; results tailored to each user given one's past book ratings and to-read list.
[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"
MoodRiser is a web application created during a 24-hour hackathon at the CodeForAll Fullstack Programming Bootcamp. Utilizing HTML, CSS, JavaScript, Python with Flask, and various APIs including Spotify and Google Books, and OpenAI, this SPA helps users manage their emotions through personalized content recommendations based on their current mood.
TrialMatchAI aims to seamlessly match cancer patients to clinical trials based on their unique genomic and clinical profiles using AI
Adaptive Applications assignments taught at Trinity College Dublin.
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