PersiaML/PERSIA
High performance distributed framework for training deep learning recommendation models based on PyTorch.
This project helps e-commerce platforms, social media companies, and streaming services build highly personalized recommendation systems. It takes user interaction data and item information as input to generate sophisticated deep learning models capable of suggesting relevant content or products. Data scientists and machine learning engineers who need to train very large recommendation models on standard hardware would find this useful.
411 stars. No commits in the last 6 months.
Use this if you need to train extremely large-scale deep learning recommendation models, potentially with trillions of parameters, and require high efficiency on commodity hardware.
Not ideal if you are looking for a maintained project with stable, comprehensive English documentation and tutorials.
Stars
411
Forks
54
Language
Rust
License
MIT
Category
Last pushed
Jun 14, 2025
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/PersiaML/PERSIA"
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