Due to the excellent capacities of large language models (LLMs), it becomes feasible to develop LLM-based agents for reliable user simulation. Considering the scarcity and limit (e.g., privacy issues) of real user data, in this paper, we conduct large-scale user simulation for web search, to improve the analysis and modeling of user search behavior. Specially, we propose BASES, a novel user simulation framework with LLM-based agents, designed to facilitate comprehensive simulations of web search user behaviors. Our simulation framework can generate unique user profiles at scale, which subsequently leads to diverse search behaviors. To demonstrate the effectiveness of BASES, we conduct evaluation experiments based on two human benchmarks in both Chinese and English, demonstrating that BASES can effectively simulate large-scale human-like search behaviors. To further accommodate the research on web search, we develop WARRIORS, a new large-scale dataset encompassing web search user behaviors, including both Chinese and English versions, which can greatly bolster research in the field of information retrieval. Our code and data will be publicly released soon.

翻译：摘要：鉴于大型语言模型（LLMs）的卓越能力，开发基于LLM的智能体以实现可靠的用户模拟已成为可行方案。考虑到真实用户数据的稀缺性及局限性（如隐私问题），本文针对网络搜索场景开展大规模用户模拟，以改进用户搜索行为的分析与建模。具体而言，我们提出BASES这一新的基于LLM智能体的用户模拟框架，旨在支持对网络搜索用户行为的全面模拟。该模拟框架能够规模化生成独特的用户画像，并由此衍生出多样化的搜索行为。为验证BASES的有效性，我们基于中英文两个人工基准进行评测实验，结果表明BASES能够有效模拟大规模类人搜索行为。此外，为促进网络搜索研究，我们构建了WARRIORS——一个涵盖网络搜索用户行为的大规模新数据集（包含中英文版本），这将极大推动信息检索领域的研究。我们的代码与数据将稍后公开发布。