发表机构
University of Duisburg-Essen; TH Köln - University of Applied Sciences（杜伊斯堡-埃森大学; 科隆应用技术大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对经典马尔可夫模型难以整合上下文信息的问题，本文提出特征条件马尔可夫用户模型，通过将转移概率建模为位置、内容及交互特征的函数，实现上下文感知决策，并验证了其在不同场景下提升用户模拟真实性的效果。
AI 中文摘要
用户行为模拟被广泛用于评估交互式信息检索系统，但经典的基于状态的方法（如马尔可夫模型）在整合与决策相关的上下文信息方面能力有限。我们通过引入一种特征条件的马尔可夫风格用户模型来解决这一局限性，在该模型中，转移概率被建模为位置、内容及交互衍生特征的函数，从而在保持基于状态模型的结构简洁性和计算效率的同时，实现上下文感知的决策。我们采用一个评估预测拟合度和行为保真度的多层次框架，分析了不同来源的上下文信息如何在多个数据集、搜索设置和特征配置中促进逼真的用户模拟。我们的结果表明，纳入上下文特征能提高模型再现真实用户交互关键方面的能力，但其有效性取决于搜索场景和建模目标。有效的模拟并非依赖一刀切的解决方案，而是需要针对任务和设置进行特征选择。我们的框架为做出这些选择提供了实用且可解释的基础。
英文摘要
User behavior simulation is widely used to evaluate interactive information retrieval systems, but classical state-based approaches (e.g., Markov models) have limited ability to incorporate contextual information relevant for decision-making. We address this limitation by introducing a feature-conditioned Markov-style user model, in which transition probabilities are modeled as functions of positional, content-based, and interaction-derived features, enabling context-aware decision making while preserving the structural simplicity and computational efficiency of state-based models. Applying a multi-level framework that assesses predictive fit and behavioral fidelity, we analyze how different sources of contextual information contribute to realistic user simulation across multiple datasets, search settings, and feature configurations. Our results show that incorporating contextual features improves the models' ability to reproduce key aspects of real user interactions, but that their effectiveness hinges on search scenario and modeling objective. Instead of a one-size-fits-all solution, effective simulation requires task- and setting-specific feature selection. Our framework provides a practical and interpretable basis for making these choices.
CommentsThis is the author's version of the work. It is posted here for your personal use. Not for redistribution. The definitive Version of Record was published in Proceedings of the 35th ACM International Conference on Information and Knowledge Management (CIKM '26), dx.doi.org/10.1145/3799682.3840602
