RUCAIBox/LLMRank
[ECIR'24] Implementation of "Large Language Models are Zero-Shot Rankers for Recommender Systems"
This project helps e-commerce managers, content curators, or anyone managing a recommendation system to quickly evaluate how Large Language Models (LLMs) can rank items for users without needing to train a new model. You provide user interaction histories and a list of candidate items, and it uses an LLM to generate a personalized ranking of those items. The output is a ranked list of recommendations, which you can then use to improve your system's performance.
317 stars. No commits in the last 6 months.
Use this if you want to explore the potential of LLMs for personalized item ranking in your recommender system, especially for quick, zero-shot evaluations without extensive model training.
Not ideal if you prefer to build traditional, domain-specific ranking models from scratch or if you need to deploy a highly optimized, low-latency ranking solution without reliance on external LLM APIs.
Stars
317
Forks
28
Language
Python
License
MIT
Category
Last pushed
May 15, 2025
Commits (30d)
0
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