Session search, the task of document retrieval for a series of queries in a session, has been receiving increasing attention from the information retrieval research community. Session search exhibits the properties of rich user-system interactions and temporal dependency. These properties lead to our proposal of using partially observable Markov decision process to model session search. On the basis of a design choice schema for states, actions and rewards, we evaluate different combinations of these choices over the TREC 2012 and 2013 session track datasets. According to the experimental results, practical design recommendations for using PODMP in session search are discussed.

中文翻译：

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通过部分可观察的马尔可夫决策过程进行会话搜索建模

会话搜索是会话中一系列查询的文档检索任务，已经受到信息检索研究界的越来越多的关注。会话搜索展现出丰富的用户系统交互和时间依赖性的特性。这些属性导致我们提出了使用部分可观察的马尔可夫决策过程为会话搜索建模的建议。基于状态，动作和奖励的设计选择方案，我们在TREC 2012和2013会话轨道数据集中评估这些选择的不同组合。根据实验结果，讨论了在会话搜索中使用PODMP的实用设计建议。