User behavior analysis is crucial in human-centered AI applications. In this field, the collection of sufficient and high-quality user behavior data has always been a fundamental yet challenging problem. An intuitive idea to address this problem is automatically simulating the user behaviors. However, due to the subjective and complex nature of human cognitive processes, reliably simulating the user behavior is difficult. Recently, large language models (LLM) have obtained remarkable successes, showing great potential to achieve human-like intelligence. We argue that these models present significant opportunities for reliable user simulation, and have the potential to revolutionize traditional study paradigms in user behavior analysis. In this paper, we take recommender system as an example to explore the potential of using LLM for user simulation. Specifically, we regard each user as an LLM-based autonomous agent, and let different agents freely communicate, behave and evolve in a virtual simulator called RecAgent. For comprehensively simulation, we not only consider the behaviors within the recommender system (\emph{e.g.}, item browsing and clicking), but also accounts for external influential factors, such as, friend chatting and social advertisement. Our simulator contains at most 1000 agents, and each agent is composed of a profiling module, a memory module and an action module, enabling it to behave consistently, reasonably and reliably. In addition, to more flexibly operate our simulator, we also design two global functions including real-human playing and system intervention. To evaluate the effectiveness of our simulator, we conduct extensive experiments from both agent and system perspectives. In order to advance this direction, we have released our project at {https://github.com/RUC-GSAI/YuLan-Rec}.

翻译：用户行为分析在以人为中心的人工智能应用中至关重要。在该领域，获取充足且高质量的用户行为数据始终是基础且具挑战性的问题。解决该问题的一个直观思路是自动模拟用户行为。然而，由于人类认知过程的主观性和复杂性，可靠地模拟用户行为存在困难。近年来，大语言模型（LLM）取得了卓越成就，展现出实现类人智能的巨大潜力。我们认为，这类模型为可靠的用户模拟提供了重要机遇，并有望彻底改变用户行为分析领域的传统研究范式。本文以推荐系统为例，探索利用LLM进行用户模拟的潜力。具体而言，我们将每位用户视为基于LLM的自主智能体，让不同智能体在名为RecAgent的虚拟模拟器中自由沟通、行动与演化。为实现全面模拟，我们不仅考虑推荐系统内部行为（如物品浏览与点击），还纳入外部影响因素（如好友聊天和社会广告）。我们的模拟器包含至多1000个智能体，每个智能体由画像模块、记忆模块和行动模块组成，使其行为具有一致性、合理性和可靠性。此外，为更灵活地操作模拟器，我们设计了两种全局功能，包括真人介入和系统干预。为评估模拟器的有效性，我们从智能体和系统两个角度进行了大量实验。为推动该方向研究，我们已在{https://github.com/RUC-GSAI/YuLan-Rec}开源项目。