The task of session search focuses on using interaction data to improve relevance for the user's next query at the session level. In this paper, we formulate session search as a personalization task under the framework of learning to rank. Personalization approaches re-rank results to match a user model. Such user models are usually accumulated over time based on the user's browsing behaviour. We use a pre-computed and transparent set of user models based on concepts from the social science literature. Interaction data are used to map each session to these user models. Novel features are then estimated based on such models as well as sessions' interaction data. Extensive experiments on test collections from the TREC session track show statistically significant improvements over current session search algorithms.

中文翻译：

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学习个性化网页搜索会话

会话搜索的任务侧重于使用交互数据在会话级别提高用户下一个查询的相关性。在本文中，我们将会话搜索制定为学习排名框架下的个性化任务。个性化方法重新排列结果以匹配用户模型。这些用户模型通常是基于用户的浏览行为随时间累积的。我们使用基于社会科学文献中的概念的预先计算和透明的用户模型集。交互数据用于将每个会话映射到这些用户模型。然后基于这些模型以及会话的交互数据来估计新特征。对来自 TREC 会话轨道的测试集进行的大量实验表明，对当前会话搜索算法的统计显着改进。