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

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arxiv 2403.06465 v1 pith:4FBSHEQZ submitted 2024-03-11 cs.IR cs.AI

Jianxun Lian , Yuxuan Lei , Xu Huang , Jing Yao , Wei Xu , Xing Xie This is my paper
classification cs.IRcs.AI
keywords recairecommendersystemslanguagellmsmodelsadvancedlarge
verification ladder T0 review T1 audit T2 compute T3 formal
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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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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping

    cs.IR 2026-06 conditional novelty 6.0 of 10

    A single LLM trained to emit semantic item codes can fulfill complex shopping intents with fewer tool hand-offs, improving multi-turn follow-up on Taobao-derived tasks.

  2. ShopX: A Foundation Model for Intent-to-Item Fulfillment in Agentic Shopping

    cs.IR 2026-06 unverdicted novelty 5.0 of 10

    ShopX is a single foundation model combining intent understanding, planning, and SID-native item fulfillment for agentic shopping, with claimed improvements over tool-mediated systems on Taobao logs.

  3. Offline Evaluation Measures of Fairness in Recommender Systems

    cs.IR 2026-04 unverdicted novelty 4.0 of 10

    The thesis identifies theoretical, empirical, and conceptual flaws in offline fairness measures for recommender systems and contributes new evaluation methods and practical guidelines.

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