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：用户交互模拟能够实现更面向用户的信息检索（IR）系统评估。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常缺乏保真度。最值得注意的是，现有用户模型忽略了用户上下文——这是驱动相关性感知和搜索结果互动的主要因素。为此，本研究引入了基于上下文的查询重构模拟。所提出的查询生成方法基于最新大型语言模型（LLM）技术，并在整个搜索会话模拟过程中考虑用户上下文。与简单的无上下文查询生成方法相比，这些方法展现出更优的有效性，并能模拟更高效的IR会话。类似地，我们的评估方法比当前基于会话的度量标准考虑了更多交互上下文，在既有评估协议之外揭示了有趣的互补性见解。最后，我们提出了未来研究方向，并提供了完全开放的实验环境。