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Cited by 94Recommender Systems and TechniquesInformation Retrieval and Search BehaviorIntelligent Tutoring Systems and Adaptive Learning

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems

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Zhang, Junjie, Hou, Yupeng, Xie, Ruobing, Sun, Wenqi, McAuley, Julian, Zhao, Wayne Xin, Lin, Leyu, Wen, Ji-Rong·2024·Proceedings of the ACM Web Conference 2024
DOI 10.1145/3589334.3645537
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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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  • Prior memory-augmented embodied agent approaches fail to address challenges such as hallucinations or memory inconsistencies that often arise in long-sequence task planning with LLMs.

    “However, these approaches fail to address challenges such as hallucinations or memory inconsistencies that often arise long-sequence task planning with LLMs.”

    From KARMA: Augmenting Embodied AI Agents with Long-and-Short Term Memory Systems (2025) · cites this paper as Zhang et al., 2023

    Section 2.2.1 describes memory modules for recommender-system agents, and Appendix B.3 discusses correcting misleading preferences and the practical limits of storing long interaction histories. Neither passage addresses whether prior memory-augmented embodied agents fail to handle hallucinations or memory inconsistencies in long-sequence task planning; searches for those challenges found no supporting passage. (LLM, conf=0.76)

    Experimental — accuracy not yet measured

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References 15 · Cited by 94

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Authors
Zhang, J., Hou, Y., Xie, R., Sun, W., McAuley, J., Zhao, W. X., Lin, L., & Wen, J.-R.
Affiliations
  • Renmin University of China (CN)
  • University of California San Diego (US)
  • Tencent (China) (CN)
ORCID
Venue
Proceedings of the ACM Web Conference 2024
Publisher
ACM
Published
2024-05-13
Pages
3679-3689
DOI
10.1145/3589334.3645537
OpenAlex
W4396758674
Cited by
94
References
15
Citations by year
2026: 44 · 2025: 35 · 2024: 15
FWCI
35.71
Type
conference paper
Language
en
Open access
Licence
Locations
  • International World Wide Web Conference (WWW) (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, cc-by)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
Keywords
cooperative learning, recommender systems, multi-agent systems

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