Clustering search engine log for query recommendation

Abstract

As web contents grow, the importance of search engines became more critical and at the same time user satisfaction decreased. Query recommendation is a new approach to improve search results in web. In this paper we represent a method to help search engine users in attaining required information. Such facility could be provided by offering some queries associated with queries submitted by users in order to direct them toward their target. At first, all previous query contained in a query log should be clustered, therefore, all queries that are semantically similar will be detected. Then all queries that are similar to user's queries are ranked according to a relevance criterion. The method has been evaluated using a real world data set and by comparing it to existing approaches, the results show promising improvements. © 2008 Springer-Verlag.