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Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search

Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search

Suruchi Chawla
Copyright: © 2017 |Pages: 26
ISBN13: 9781522518778|ISBN10: 1522518770|EISBN13: 9781522518785
DOI: 10.4018/978-1-5225-1877-8.ch008
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MLA

Chawla, Suruchi. "Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search." Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, IGI Global, 2017, pp. 131-156. https://doi.org/10.4018/978-1-5225-1877-8.ch008

APA

Chawla, S. (2017). Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search. In G. Sreedhar (Ed.), Web Data Mining and the Development of Knowledge-Based Decision Support Systems (pp. 131-156). IGI Global. https://doi.org/10.4018/978-1-5225-1877-8.ch008

Chicago

Chawla, Suruchi. "Multi-Agent-Based Information Retrieval System Using Information Scent in Query Log Mining for Effective Web Search." In Web Data Mining and the Development of Knowledge-Based Decision Support Systems, edited by G. Sreedhar, 131-156. Hershey, PA: IGI Global, 2017. https://doi.org/10.4018/978-1-5225-1877-8.ch008

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Abstract

This chapter explains the multi-agent system for effective information retrieval using information scent in query log mining. The precision of search results is low due to difficult to infer the information need of the small size search query and therefore information need of the user is not satisfied effectively. Information Scent is used for modeling the information need of user web search session and clustering is performed to identify the similar information need sessions. Hyper Link-Induced Topic Search (HITS) is executed on clusters to generate the Hubs and authorities for web page recommendations to users who search with similar intents. This multi-agent system based on clustered query sessions uses query operations like expansion and recommendation to infer the information need of user search queries and recommends Hubs and authorities for effective web search.

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