Mining Web Resources for Enhancing Information Retrieval
Improving search engines by query clustering
Article first published online: 6 SEP 2007
Copyright © 2007 Wiley Periodicals, Inc.
Journal of the American Society for Information Science and Technology
Volume 58, Issue 12, pages 1793–1804, October 2007
How to Cite
Baeza-Yates, R., Hurtado, C., Mendoza, M. (2007), Improving search engines by query clustering. J. Am. Soc. Inf. Sci., 58: 1793–1804. doi: 10.1002/asi.20627
- Issue published online: 24 SEP 2007
- Article first published online: 6 SEP 2007
- Manuscript Accepted: 4 JAN 2007
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.