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Recently, there has been an emergence of employing LLM-powered agents asbelievable human proxies, based on their remarkable decision-making capability.However, existing studies mainly focus on simulating human dialogue. Humannon-verbal behaviors, such as item clicking in recommender systems, althoughimplicitly exhibiting user preferences and could enhance the modeling of users,have not been deeply explored. The main reasons lie in the gap between languagemodeling and behavior modeling, as well as the incomprehension of LLMs aboutuser-item relations.  To address this issue, we propose AgentCF for simulating user-iteminteractions in recommender systems through agent-based collaborativefiltering. We creatively consider not only users but also items as agents, anddevelop a collaborative learning approach that optimizes both kinds of agentstogether. Specifically, at each time step, we first prompt the user and itemagents to interact autonomously. Then, based on the disparities between theagents' decisions and real-world interaction records, user and item agents areprompted to reflect on and adjust the misleading simulations collaboratively,thereby modeling their two-sided relations. The optimized agents can alsopropagate their preferences to other agents in subsequent interactions,implicitly capturing the collaborative filtering idea. Overall, the optimizedagents exhibit diverse interaction behaviors within our framework, includinguser-item, user-user, item-item, and collective interactions. The results showthat these agents can demonstrate personalized behaviors akin to those ofreal-world individuals, sparking the development of next-generation userbehavior simulation.

AgentCF: Collaborative Learning with Autonomous Language Agents for Recommender Systems