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：模拟用户交互能够实现更以用户为导向的信息检索（IR）系统评估。虽然用户模拟具有成本效益且可重复，但许多方法在模拟真实用户行为方面通常缺乏保真度。最显著的是，当前的用户模型忽略了用户上下文——而上下文正是感知相关性与搜索结果交互的主要驱动力。为此，本研究引入了基于上下文的查询重构模拟方法。所提出的查询生成方法基于当前的大语言模型（LLM）技术，并在模拟搜索会话过程中持续考虑用户上下文。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，并能模拟更高效的IR会话。同样，我们的评估比当前的会话级指标包含了更多交互上下文，并在既有评估协议之外揭示了有益的补充性见解。最后，我们指出了未来工作方向，并提供了完全开放的实验设置。