RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866916155776565248 |
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| author | Lian, Jianxun Lei, Yuxuan Huang, Xu Yao, Jing Xu, Wei Xie, Xing |
| author_facet | Lian, Jianxun Lei, Yuxuan Huang, Xu Yao, Jing Xu, Wei Xie, Xing |
| contents | 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}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_06465 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems Lian, Jianxun Lei, Yuxuan Huang, Xu Yao, Jing Xu, Wei Xie, Xing Information Retrieval Artificial Intelligence 68T50 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}. |
| title | RecAI: Leveraging Large Language Models for Next-Generation Recommender Systems |
| topic | Information Retrieval Artificial Intelligence 68T50 |
| url | https://arxiv.org/abs/2403.06465 |