sober-clever/ReRe

The implementations of paper "Reinforced Preference Optimization for Recommendation" (ReRe).

28
/ 100
Experimental

This project helps e-commerce and content platforms improve their recommendation systems. By taking existing user preference data, it outputs optimized recommendations that are more aligned with what users actually want. This is designed for data scientists and machine learning engineers working on improving user experience and engagement.

Use this if you are a data scientist or ML engineer looking to enhance your recommendation engine's performance beyond standard methods.

Not ideal if you need a plug-and-play solution without advanced machine learning expertise or access to detailed user preference data.

recommendation-systems e-commerce content-personalization machine-learning-engineering user-engagement
No License No Package No Dependents
Maintenance 6 / 25
Adoption 6 / 25
Maturity 7 / 25
Community 9 / 25

How are scores calculated?

Stars

17

Forks

2

Language

Python

License

—

Category

llm-recommendation-systems

Last pushed

Nov 16, 2025

Commits (30d)

0

GitHub
Llm Recommendation Systems · 25 models

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