AgentCF: Collaborative Learning with Autonomous Language Agents for
  Recommender Systems
Junjie Zhang Yupeng Hou Ruobing Xie Wenqi Sun Julian McAuley Wayne Xin Zhao Leyu Lin Ji-Rong Wen
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.