Improving search engines by query clustering

Ricardo Baeza‐Yates, Carlos Hurtado and Marcelo Mendoza

Journal of the American Society for Information Science and Technology, 2007, vol. 58, issue 12, 1793-1804

Abstract: 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.

Date: 2007
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https://doi.org/10.1002/asi.20627

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Persistent link: https://EconPapers.repec.org/RePEc:bla:jamist:v:58:y:2007:i:12:p:1793-1804

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