alibaba/Dynamic-popularity-aware-recommendation
Dynamic popularity-aware contrastive learning for recommendation
This tool helps e-commerce platforms and content providers offer better recommendations to their users. It takes historical user interaction data with various items and generates personalized suggestions. The primary users are recommendation system practitioners and data scientists working on improving user engagement and sales for online services.
No commits in the last 6 months.
Use this if you need to build or enhance a recommendation system that can adapt to changing item popularity and user interests over time.
Not ideal if you are looking for a simple, off-the-shelf recommendation solution without diving into model training and evaluation.
Stars
10
Forks
5
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Jun 16, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/alibaba/Dynamic-popularity-aware-recommendation"
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
google-research/recsim
A Configurable Recommender Systems Simulation Platform