Improving search engines by query clustering

In this paper, we present a framework for clustering Web search engine queries whose aim is to identify groups of queries used to search for similar information on the Web. The framework is based on a novel term vector model of queries that integrates user selections and the content of selected documents extracted from the logs of a search engine. The query representation obtained allows us to treat query clustering similarly to standard document clustering. We study the application of the clustering framework to two problems: relevance ranking boosting and query recommendation. Finally, we evaluate with experiments the effectiveness of our approach.