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A k-Nearest-Neighbour Method for Classifying Web Search Results with Data in Folksonomies (2008)

Abstract
Traditional Web search engines mostly adopt a keyword-based approach. When the keyword submitted by the user is ambiguous, search result usually consists of documents related to various meanings of the keyword, while the user is probably interested in only one of them. In this paper we attempt to provide a solution to this problem using a k-nearest-neighbour approach to classify documents returned by a search engine, by building classifiers using data collected from collaborative tagging systems. Experiments on search results returned by Google show that our method is able to classify the documents returned with high precision.

Publication details
Download http://eprints.ecs.soton.ac.uk/16991/1/wi08_websearch_presentation.pdf
http://eprints.ecs.soton.ac.uk/16991/2/cmauyeung-WebSearchFolksonomy.pdf
Repository University of Southampton [School of Electronics and Computer Science] (United Kingdom)
Type Conference or Workshop Item, PeerReviewed
Relation http://eprints.ecs.soton.ac.uk/16991/