Neural Graph Collaborative Filtering, SIGIR2019
👕 Open-source course on architecting, building and deploying a real-time personalized recommender for H&M fashion articles.
📚 PaperFlow: Dynamic personalized scientific-paper recommendation, reading, and reporting
Disentagnled Graph Collaborative Filtering, SIGIR2020
[ACMMM 2021] PyTorch implementation for "Mining Latent Structures for Multimedia Recommendation"
Code and dataset for CVPR 2019 paper "Learning Binary Code for Personalized Fashion Recommendation"
TrialMatchAI leverages large language models to streamline clinical trial matching by evaluating patient-specific clinical characteristics against trial eligibility criteria and generating relevant, ranked trial recommendations.
Priveedly: A django-based content reader and recommender for personal and private use
Hybrid book recommendation system fusing a scikit-learn k-nearest-neighbors collaborative filtering model with a TF-IDF cosine-similarity content model, weighted-average combined and trained on the 1.1 million rating Book-Crossing dataset, served through a FastAPI backend with TTL caching, a decoupled React frontend, and Docker Compose deployment.
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 recommendation
Personalized Visual Art Recommendation by Learning Latent Semantic Representations
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.
This system helps users discover personalized audiobook recommendations based on their preferred genre, author, or book title, backed by data-driven insights and visualizations.
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.
Privacy-aware ML pipeline for consumer behavior mining, customer segmentation, promotion-response prediction, and low-interruption recommendations.
Analyzing Temporal, Spatial, and Historical Data in Rating Prediction Algorithms: A Comparative Study
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