论文标题
用户模拟方法的艺术状态，以进行对话信息检索
State of the Art of User Simulation approaches for conversational information retrieval
论文作者
论文摘要
会话信息检索（CIR）是在交互式IR和对话系统的交互中，满足开放域信息需求的相交中的信息检索（IR）。为了优化这些交互并增强用户体验，有必要通过考虑顺序的异质用户系统交互来改善IR模型。强化学习已成为一种范式，特别适合优化许多领域中的顺序决策，并且最近出现在IR中。但是，通过对用户的增强学习来培训这些系统是不可行的。一种解决方案是对IR系统进行训练，以模拟真实用户的行为的用户模拟。我们的贡献是双重的：1）查看有关信息访问的用户建模和用户仿真的文献，以及2）在CIR中讨论用户模拟的不同研究观点
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
