research-article

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

Authors:

Julian McAuley,

Wayne Xin Zhao,

Ji-Rong WenAuthors Info & Claims

WWW '24: Proceedings of the ACM Web Conference 2024

Pages 3679 - 3689

https://doi.org/10.1145/3589334.3645537

Published: 13 May 2024 Publication History

Abstract

Recently, there has been an emergence of employing LLM-powered agents as believable human proxies, based on their remarkable decision-making capability. However, existing studies mainly focus on simulating human dialogue. Human non-verbal behaviors, such as item clicking in recommender systems, although implicitly exhibiting user preferences and could enhance the modeling of users, have not been deeply explored. The main reasons lie in the gap between language modeling and behavior modeling, as well as the incomprehension of LLMs about user-item relations.

To address this issue, we propose AgentCF for simulating user-item interactions in recommender systems through agent-based collaborative filtering. We creatively consider not only users but also items as agents, and develop a collaborative learning approach that optimizes both kinds of agents together. Specifically, at each time step, we first prompt the user and item agents to interact autonomously. Then, based on the disparities between the agents' decisions and real-world interaction records, user and item agents are prompted to reflect on and adjust the misleading simulations collaboratively, thereby modeling their two-sided relations. The optimized agents can also propagate their preferences to other agents in subsequent interactions, implicitly capturing the collaborative filtering idea. Overall, the optimized agents exhibit diverse interaction behaviors within our framework, including user-item, user-user, item-item, and collective interactions. The results show that these agents can demonstrate personalized behaviors akin to those of real-world individuals, sparking the development of next-generation user behavior simulation.

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Index Terms

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems
1. Information systems
  1. Information retrieval

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cover image ACM Conferences

WWW '24: Proceedings of the ACM Web Conference 2024

May 2024

4826 pages

ISBN:9798400701719

DOI:10.1145/3589334

General Chairs:
Tat-Seng Chua
National University of Singapore
,
Chong-Wah Ngo
Singapore Management University
,
Proceedings Chair:
Roy Ka-Wei Lee
Singapore University of Technology and Design
,
Program Chairs:
Ravi Kumar
Google
,
Hady W. Lauw
Singapore Management University

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Published: 13 May 2024

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WWW '24: The ACM Web Conference 2024

May 13 - 17, 2024

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https://doi.org/10.1109/OJCS.2024.3509221
Duan SOuyang MWang RLi QXiao Y(2025)Let long-term interests talk: An disentangled learning model for recommendation based on short-term interests generationInformation Processing & Management10.1016/j.ipm.2024.10399762:2(103997)Online publication date: Mar-2025
https://doi.org/10.1016/j.ipm.2024.103997
Takata RMasumori AIkegami T(2024)Spontaneous Emergence of Agent Individuality Through Social Interactions in Large Language Model-Based CommunitiesEntropy10.3390/e2612109226:12(1092)Online publication date: 13-Dec-2024
https://doi.org/10.3390/e26121092
Qu ZXie RXiao CYao YLiu ZLian FKang ZZhou J(2024)Thoroughly Modeling Multi-domain Pre-trained Recommendation as LanguageACM Transactions on Information Systems10.1145/3708883Online publication date: 19-Dec-2024
https://doi.org/10.1145/3708883
Lin JDai XXi YLiu WChen BZhang HLiu YWu CLi XZhu CGuo HYu YTang RZhang W(2024)How Can Recommender Systems Benefit from Large Language Models: A SurveyACM Transactions on Information Systems10.1145/3678004Online publication date: 13-Jul-2024
https://doi.org/10.1145/3678004
Zhang ADeng YLin YChen XWen JChua THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)Large Language Model Powered Agents for Information RetrievalProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3661375(2989-2992)Online publication date: 10-Jul-2024
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Deng YZhang ALin YChen XWen JChua TChua TNgo CKumar RLauw HKa-Wei Lee R(2024)Large Language Model Powered Agents in the WebCompanion Proceedings of the ACM Web Conference 202410.1145/3589335.3641240(1242-1245)Online publication date: 13-May-2024
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Liu YWang YCao YSun LYu P(2024)Visual Summary Thought of Large Vision-Language Models for Multimodal Recommendation2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825030(456-461)Online publication date: 15-Dec-2024
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Li XWang SZeng SWu YYang Y(2024)A survey on LLM-based multi-agent systems: workflow, infrastructure, and challengesVicinagearth10.1007/s44336-024-00009-21:1Online publication date: 8-Oct-2024
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Wu LZheng ZQiu ZWang HGu HShen TQin CZhu CZhu HLiu QXiong HChen E(2024)A survey on large language models for recommendationWorld Wide Web10.1007/s11280-024-01291-227:5Online publication date: 22-Aug-2024
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