liuqidong07/LLMEmb
[AAAI'25 Oral] The official implementation code of LLMEmb
This project helps e-commerce and content platforms improve their recommendation systems. It takes raw user interaction data and item descriptions (like product reviews or movie synopses) and generates highly effective item embeddings using large language models. The output is a set of refined item embeddings and a trained recommendation model, used by data scientists or machine learning engineers to build better sequential recommendation systems.
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Use this if you need to leverage the power of large language models to create sophisticated embeddings for items in a sequential recommendation system.
Not ideal if you are looking for a plug-and-play recommendation system and do not have machine learning expertise to configure and fine-tune models.
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52
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Language
Python
License
MIT
Category
Last pushed
Mar 13, 2025
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