Abstract: When a user input a query, intelligent search engine can suggest a list of related queries. Query recommendation is the method to improve the search results on web. This paper presents the method of mining the search engine query log to get the fast query Recommendation from large scale. In this, a formula is applied to fast recommend the most related queries for the user with useful information. For this, technology used for allowing query recommendation is query log which contains the attributes like query name, document which contains term occurred in the document , normalization factor, inverse document frequency and it also helps in minimize the retrieval time of user submitted query. As a result it shows the user most relevant queries to make user to find the query easily and satisfy their needs.
Keywords: Query log, Search engine, Clustering, Query similarity, Information retrieval (IR), Document frequency (df), Term frequency (tf), Inverse document frequency (idf), Normalization.
Title: QUERY RECOMMENDATION WITH SIMILARITY IMPROVEMENT IN QUERY LOG
Author: Ritu Maheshwari Bansal, Meenakshi Bhdadana
International Journal of Computer Science and Information Technology Research
ISSN 2348-1196 (print), ISSN 2348-120X (online)
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