yihong-chen/neural-collaborative-filtering
pytorch version of neural collaborative filtering
This project helps you build a recommendation system by learning how users interact with items. You provide data on user preferences (e.g., movie ratings), and it generates predictions about which items a user might like. This is useful for anyone building or evaluating content, product, or service recommendation engines.
510 stars. No commits in the last 6 months.
Use this if you are a machine learning engineer or data scientist looking for a PyTorch-based implementation to experiment with neural collaborative filtering models for recommendations.
Not ideal if you need an out-of-the-box recommendation system for direct deployment without writing or modifying code.
Stars
510
Forks
162
Language
Jupyter Notebook
License
—
Category
Last pushed
Jun 23, 2024
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yihong-chen/neural-collaborative-filtering"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
meta-pytorch/torchrec
Pytorch domain library for recommendation systems
recommenders-team/recommenders
Best Practices on Recommendation Systems
hongleizhang/RSPapers
RSTutorials: A Curated List of Must-read Papers on Recommender System.
kakao/buffalo
TOROS Buffalo: A fast and scalable production-ready open source project for recommender systems
RUCAIBox/CRSLab
CRSLab is an open-source toolkit for building Conversational Recommender System (CRS).