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 obtained from a major commercial search engine. Our evaluation uses both human judgments and implicit user click data. The experimental results clearly show that our context-aware ranking approach improves the ranking of a commercial search engine which ignores context information. Furthermore, our method outperforms a baseline method which considers context information in ranking.
context-aware ranking context ranking web search search query search engine developer context model model scale laboratory critic types integrability informal paper encode feature test log data evaluation human user experimentation method learning-to-rank application art context aware learning to rank