Conversational Information Retrieval (CIR) is an emerging field of Information Retrieval (IR) at the intersection of interactive IR and dialogue systems for open domain information needs. In order to optimize these interactions and enhance the user experience, it is necessary to improve IR models by taking into account sequential heterogeneous user-system interactions. Reinforcement learning has emerged as a paradigm particularly suited to optimize sequential decision making in many domains and has recently appeared in IR. However, training these systems by reinforcement learning on users is not feasible. One solution is to train IR systems on user simulations that model the behavior of real users. Our contribution is twofold: 1)reviewing the literature on user modeling and user simulation for information access, and 2) discussing the different research perspectives for user simulations in the context of CIR
翻译:信息相互检索(CIR)是一个新兴的信息检索(IR)领域,位于互动的IR和开放域信息需要的对话系统交汇处,信息检索(IR)是信息检索(IR)的一个新兴领域。为了优化这些互动,提高用户的经验,有必要通过考虑到相继不同用户-系统的互动来改进IR模型。强化学习已成为一个特别适合优化许多领域顺序决策的范例,最近又出现在IR。然而,通过强化用户学习来培训这些系统并不可行。一个解决办法是培训IR系统进行用户模拟,以模拟真实用户的行为。我们的贡献有两个方面:(1)审查关于用户建模和信息存取用户模拟的文献;(2)在CIR范围内讨论用户模拟的不同研究观点。