Jyonn/Legommenders
(TheWebConf'25) Official Library for the Paper "Legommenders: A Comprehensive Content-Based Recommendation Library with LLM Support"
Legommenders helps you build and evaluate content-based recommendation systems using large language models. You input existing user interaction data (like news clicks, movie watches, or product purchases) and it outputs trained recommendation models ready to suggest new items. This is designed for researchers and practitioners in fields like e-commerce, media, or content platforms who need to benchmark or develop advanced recommendation engines.
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Use this if you are developing or evaluating content-based recommendation models, especially those leveraging large language models, across diverse domains like news, books, movies, music, or e-commerce.
Not ideal if you need a plug-and-play recommendation API for direct deployment without custom model training and evaluation.
Stars
30
Forks
3
Language
Python
License
MIT
Category
Last pushed
Sep 22, 2025
Commits (30d)
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