Context-aware ranking in web search

The context of a search query often provides a search engine meaningful hints for answering the current query better. Previous studies on context-aware search were either focused on the development of context models or limited to a relatively small scale investigation under a controlled laboratory setting. Particularly, about context-aware ranking for Web search, the following two critical problems are largely remained unsolved. First, how can we take advantage of different types of contexts in ranking? Second, how can we integrate context information into a ranking model? In this paper, we tackle the above two essential problems analytically and empirically. We develop different ranking principles for different types of contexts. Moreover, we adopt a learning-to-rank approach and integrate the ranking principles into a state-of-the-art ranking model by encoding the context information as features of the model. We empirically test our approach using a large search log data set obta...

• Type: Conference
• Year: 2010
• Where: SIGIR
• Authors: Biao Xiang, Daxin Jiang, Jian Pei, Xiaohui Sun, Enhong Chen, Hang Li
• Keywords: Context Information, Context-aware Ranking, Information Management, Ranking, SIGIR 2010