RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems

Large Language Models (LLMs) offer significant potential for the development of cutting-edge recommender systems, particularly in terms of enhancing interactivity, explainability, and controllability. These are aspects that have traditionally posed challenges. However, the direct application of a general-purpose LLM for recommendation purposes is not viable due to the absence of specific domain knowledge.

The RecAI project aims to bridge this gap by investigating various strategies to integrate LLMs into recommender systems, a concept people usually term as LLM4Rec. Our goal is to reflect the real-world needs of LLM4Rec through holistic views and methodologies.

We believe that by adopting a holistic perspective, we can incorporate the majority of practical requirements of LLM4Rec into one or more of the techniques explored in the RecAI project. These techniques include, but are not limited to, Recommender AI agents, the injection of knowledge through personalized prompting, fine-tuning language models as recommenders, evaluation, and LLMs as model explainers. The ultimate objective is to create a more sophisticated, interactive, and user-centric recommender system.

License

RecAI uses MIT license.

Contributing

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Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Acknowledge

Thanks to the open source codes of the following projects:

UniRec   VisualChatGPT   JARVIS   LangChain   guidance   FlagEmbedding  

Responsible AI FAQ

Please refer to RecAI: Responsible AI FAQ for document on the purposes, capabilities, and limitations of the RecAI systems.

Citation

If this project aids your research, please cite our following paper and any related paper in the respective subfolder.

@article{lian2024recai,
  title={RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems},
  author={Lian, Jianxun and Lei, Yuxuan and Huang, Xu and Yao, Jing and Xu, Wei and Xie, Xing},
  journal={arXiv preprint arXiv:2403.06465},
  year={2024}
}