论文标题
学习个性化网络搜索课程
Learning to Personalize for Web Search Sessions
论文作者
论文摘要
会话搜索的任务着重于使用交互数据来提高在会话级别上对用户的下一个查询的相关性。在本文中，我们将会话搜索作为个性化任务，在学习排名的框架下。个性化方法重新排列结果以匹配用户模型。此类用户模型通常会根据用户的浏览行为而累积。我们根据社会科学文献的概念使用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，基于此类模型以及会议的相互作用数据估算新功能。从TREC会话轨道上进行测试收集的广泛实验表明，与当前的会话搜索算法相比，统计学上显着的改进。
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.
