Simulating user interactions enables a more user-oriented evaluation of information retrieval (IR) systems. While user simulations are cost-efficient and reproducible, many approaches often lack fidelity regarding real user behavior. Most notably, current user models neglect the user's context, which is the primary driver of perceived relevance and the interactions with the search results. To this end, this work introduces the simulation of context-driven query reformulations. The proposed query generation methods build upon recent Large Language Model (LLM) approaches and consider the user's context throughout the simulation of a search session. Compared to simple context-free query generation approaches, these methods show better effectiveness and allow the simulation of more efficient IR sessions. Similarly, our evaluations consider more interaction context than current session-based measures and reveal interesting complementary insights in addition to the established evaluation protocols. We conclude with directions for future work and provide an entirely open experimental setup.

翻译：模拟用户交互可以实现对信息检索（IR）系统更加用户导向的评估。虽然用户模拟具有成本效益和可重复性，但许多方法在模拟真实用户行为方面往往缺乏保真度。最显著的问题是，当前用户模型忽略了用户的上下文，而上下文恰恰是驱动感知相关性和与搜索结果交互的主要因素。为此，本文引入了上下文驱动的查询重构模拟。所提出的查询生成方法基于近期的大语言模型（LLM）方法，并在搜索会话模拟过程中考虑用户的上下文。与简单的无上下文查询生成方法相比，这些方法展现出更高的有效性，并能够模拟更高效的IR会话。同样，我们的评估考虑了比当前基于会话的度量更多的交互上下文，并在既有的评估体系之外揭示了有趣的互补性见解。最后，我们指出了未来工作的方向，并提供了一个完全开放的实验设置。