sisinflab/warprec
Unifying Academic Rigor and Industrial Scale for Responsible, Reproducible, and Efficient Recommendation
This project helps data scientists and machine learning engineers build, train, and evaluate recommendation models. It takes in user-item interaction data and outputs trained models ready to generate recommendations, along with detailed performance metrics and carbon footprint reports. The primary users are practitioners working on product recommendations, content discovery, or personalized services.
Use this if you need a flexible and efficient framework to develop and deploy recommendation systems, from quick experiments to large-scale, distributed training.
Not ideal if you are looking for a pre-built, off-the-shelf recommendation service that requires no coding or model development.
Stars
9
Forks
1
Language
Python
License
MIT
Category
Last pushed
Mar 10, 2026
Commits (30d)
0
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