Document (#32603)

Author

Baeza-Yates, R.
Hurtado, C.
Mendoza, M.

Title

Improving search engines by query clustering

Source

Journal of the American Society for Information Science and Technology. 58(2007) no.12, S.1793-1804

Year

2007

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.

Footnote

Beitrag eines Themenschwerpunktes "Mining Web resources for enhancing information retrieval"

Theme

Suchmaschinen
Data Mining

Object

WWW

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4. Vidinli, I.B.; Ozcan, R.: New query suggestion framework and algorithms : a case study for an educational search engine (2016) 0.23

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5. Wu, Y.; Liu, Y.; Tsai, Y.-H.R.; Yau, S.-T.: Investigating the role of eye movements and physiological signals in search satisfaction prediction using geometric analysis (2019) 0.22

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