article

Improving search engines by query clustering

Authors:

Ricardo Baeza-Yates,

Carlos Hurtado,

Marcelo MendozaAuthors Info & Claims

Journal of the American Society for Information Science and Technology, Volume 58, Issue 12

Pages 1793 - 1804

Published: 01 October 2007 Publication History

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. © 2007 Wiley Periodicals, Inc.

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Bailey PMoffat AScholer FThomas PPerego RSebastiani FAslam JRuthven IZobel J(2016)UQV100Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914671(725-728)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914671
Goyal PMehala NGoyal N(2015)Incremental models for query clustering and query-context aware document clusteringInternational Journal of Knowledge and Web Intelligence10.1504/IJKWI.2015.0751675:2(146-167)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1504/IJKWI.2015.075167
Mele ILeonardi SPanconesi AFerragina PGionis A(2013)Web usage mining for enhancing search-result delivery and helping users to find interesting web contentProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433493(765-770)Online publication date: 4-Feb-2013
https://dl.acm.org/doi/10.1145/2433396.2433493
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Improving search engines by query clustering

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Journal of the American Society for Information Science and Technology Volume 58, Issue 12

October 2007

204 pages

ISSN:1532-2882

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John Wiley & Sons, Inc.

United States

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Published: 01 October 2007

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Bailey PMoffat AScholer FThomas PPerego RSebastiani FAslam JRuthven IZobel J(2016)UQV100Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2914671(725-728)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2914671
Goyal PMehala NGoyal N(2015)Incremental models for query clustering and query-context aware document clusteringInternational Journal of Knowledge and Web Intelligence10.1504/IJKWI.2015.0751675:2(146-167)Online publication date: 1-Mar-2015
https://dl.acm.org/doi/10.1504/IJKWI.2015.075167
Mele ILeonardi SPanconesi AFerragina PGionis A(2013)Web usage mining for enhancing search-result delivery and helping users to find interesting web contentProceedings of the sixth ACM international conference on Web search and data mining10.1145/2433396.2433493(765-770)Online publication date: 4-Feb-2013
https://dl.acm.org/doi/10.1145/2433396.2433493
Li LXu GYang ZDolog PZhang YKitsuregawa M(2013)An efficient approach to suggesting topically related web queries using hidden topic modelWorld Wide Web10.1007/s11280-011-0151-316:3(273-297)Online publication date: 1-May-2013
https://dl.acm.org/doi/10.1007/s11280-011-0151-3
Baeza-Yates R(2013)Similarity in Web SearchProceedings of the 6th International Conference on Similarity Search and Applications - Volume 819910.1007/978-3-642-41062-8_1(1-2)Online publication date: 2-Oct-2013
https://dl.acm.org/doi/10.1007/978-3-642-41062-8_1
Cheung JLi XAdar ETeevan JAgichtein EMaarek Y(2012)Sequence clustering and labeling for unsupervised query intent discoveryProceedings of the fifth ACM international conference on Web search and data mining10.1145/2124295.2124342(383-392)Online publication date: 8-Feb-2012
https://dl.acm.org/doi/10.1145/2124295.2124342
Navarro-Arribas GTorra VErola ACastellí-Roca J(2012)User k-anonymity for privacy preserving data mining of query logsInformation Processing and Management: an International Journal10.1016/j.ipm.2011.01.00448:3(476-487)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1016/j.ipm.2011.01.004
Bar-Yossef ZKraus NSadagopan SRamamritham KKumar ARavindra MBertino EKumar R(2011)Context-sensitive query auto-completionProceedings of the 20th international conference on World wide web10.1145/1963405.1963424(107-116)Online publication date: 28-Mar-2011
https://dl.acm.org/doi/10.1145/1963405.1963424
Panigrahi DGollapudi SKing INejdl WLi H(2011)Result enrichment in commerce search using browse trailsProceedings of the fourth ACM international conference on Web search and data mining10.1145/1935826.1935874(267-276)Online publication date: 9-Feb-2011
https://dl.acm.org/doi/10.1145/1935826.1935874
Tsikrika TDiou CVries ADelopoulos A(2011)Reliability and effectiveness of clickthrough data for automatic image annotationMultimedia Tools and Applications10.1007/s11042-010-0584-155:1(27-52)Online publication date: 1-Oct-2011
https://dl.acm.org/doi/10.1007/s11042-010-0584-1
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