Due to the advantages in the cost-efficiency and reproducibility, user simulation has become a promising solution to the user-centric evaluation of information retrieval systems. Nonetheless, accurately simulating user search behaviors has long been a challenge, because users' actions in search are highly complex and driven by intricate cognitive processes such as learning, reasoning, and planning. Recently, Large Language Models (LLMs) have demonstrated remarked potential in simulating human-level intelligence and have been used in building autonomous agents for various tasks. However, the potential of using LLMs in simulating search behaviors has not yet been fully explored. In this paper, we introduce a LLM-based user search behavior simulator, USimAgent. The proposed simulator can simulate users' querying, clicking, and stopping behaviors during search, and thus, is capable of generating complete search sessions for specific search tasks. Empirical investigation on a real user behavior dataset shows that the proposed simulator outperforms existing methods in query generation and is comparable to traditional methods in predicting user clicks and stopping behaviors. These results not only validate the effectiveness of using LLMs for user simulation but also shed light on the development of a more robust and generic user simulators. The code and data are accessible at https://github.com/Meow-E/USimAgent.

翻译：由于在成本效益和可重复性方面的优势，用户模拟已成为信息检索系统以用户为中心评估的一种有前景的解决方案。然而，准确模拟用户搜索行为长期以来一直是一个挑战，因为用户在搜索中的行为高度复杂，且由学习、推理和规划等错综复杂的认知过程所驱动。近年来，大语言模型在模拟人类水平智能方面展现出显著潜力，并已被用于构建执行各种任务的自主智能体。然而，利用大语言模型模拟搜索行为的潜力尚未得到充分探索。本文介绍了一种基于大语言模型的用户搜索行为模拟器——USimAgent。该模拟器能够模拟用户在搜索过程中的查询、点击和停止行为，从而能够为特定搜索任务生成完整的搜索会话。在真实用户行为数据集上的实证研究表明，所提出的模拟器在查询生成方面优于现有方法，在预测用户点击和停止行为方面与传统方法相当。这些结果不仅验证了使用大语言模型进行用户模拟的有效性，也为开发更稳健、更通用的用户模拟器指明了方向。代码和数据可在 https://github.com/Meow-E/USimAgent 获取。