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

ArXiv Pub Date : 2024-03-11 DOI:10.1145/3589335.3651242
Jianxun Lian, Yuxuan Lei, Xu Huang, Jing Yao, Wei Xu, Xing Xie
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Abstract

This paper introduces RecAI, a practical toolkit designed to augment or even revolutionize recommender systems with the advanced capabilities of Large Language Models (LLMs). RecAI provides a suite of tools, including Recommender AI Agent, Recommendation-oriented Language Models, Knowledge Plugin, RecExplainer, and Evaluator, to facilitate the integration of LLMs into recommender systems from multifaceted perspectives. The new generation of recommender systems, empowered by LLMs, are expected to be more versatile, explainable, conversational, and controllable, paving the way for more intelligent and user-centric recommendation experiences. We hope the open-source of RecAI can help accelerate evolution of new advanced recommender systems. The source code of RecAI is available at \url{https://github.com/microsoft/RecAI}.
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RecAI:利用大型语言模型开发新一代推荐系统
本文介绍 RecAI,这是一个实用的工具包,旨在利用大型语言模型(LLM)的先进功能来增强甚至革新推荐系统。RecAI 提供了一套工具,包括推荐人工智能代理、面向推荐的语言模型、知识插件、RecExplainer 和评估器,从多角度促进 LLMs 与推荐系统的整合。有了 LLMs 的加持,新一代的推荐系统有望变得更加通用、可解释、可对话和可控制,从而为更加智能和以用户为中心的推荐体验铺平道路。我们希望 RecAI 的开源能有助于加速新的高级推荐系统的发展。RecAI 的源代码可在 \url{https://github.com/microsoft/RecAI} 上获取。
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