Neural Graph Collaborative Filtering, SIGIR2019
Disentagnled Graph Collaborative Filtering, SIGIR2020
Code and dataset for CVPR 2019 paper "Learning Binary Code for Personalized Fashion Recommendation"
[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"
👕 An open-source course that will teach you how to build and deploy a real-time personalized recommender for H&M fashion articles.
personalized recommendation
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)
Personalized Visual Art Recommendation by Learning Latent Semantic Representations
TrialMatchAI aims to seamlessly match cancer patients to clinical trials based on their unique genomic and clinical profiles using AI
It describes the features of AWS personalize.
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.
A collaborative platform for creating and curating personalized tech learning paths. Powered by Django, it tailors content based on users' skills and preferences, integrating external educational resources. http://34.72.154.173/
A demo app to show how the implementation results look like when AWS Personalize is trained with movie lens dataset.
An intuitive movie recommendation system leveraging genre similarity with TF-IDF and cosine similarity for a personalized film discovery experience.
Movie Recommendation Anytime Anywhere
Unleash the Power of Music with Personalized Concert Recommendations.
GatorSched harnesses GPT-2 to intelligently parse and respond to queries from lifelog data, providing tailored recommendations and insights for effective scheduling
Use the Scikit-Network for PageRank algorithms including Topic-specific PR and improve the performance of various recommendation-systems using Surprise library
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