Clustering queries for better document ranking

Authors:
Yi Liu;Liangjie Zhang;Ruihua Song;Jian-Yun Nie;Ji-Rong Wen
Affiliations:
Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;Microsoft Research Asia, Beijing, China;University of Montreal, Montreal, Canada;Microsoft Research Asia, Beijing, China
Venue:
Proceedings of the 18th ACM conference on Information and knowledge management
Year:
2009

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Abstract

Different queries require different ranking methods. It is however challenging to determine what queries are similar, and how to rank documents for them. In this paper, we propose a new method to cluster queries according to the similarity determined based on URLs in their answers. We then train specific ranking models for each query cluster. In addition, a cluster-specific measure of authority is defined to favor documents from authoritative websites on the corresponding topics. The proposed approach is tested using data from a search engine. It turns out that our proposed topic-dependent models can significantly improve the search results of eight most popular categories of queries.