Query Recommendation employing Query Logs in Search Optimization

In this paper we suggest a method that, given a query presented to a search engine, proposes a list of concerned queries. The concerned queries are founded in antecedently published queries, and can be published by the user to the search engine to tune or redirect the search process. The method proposed is based on a query clustering procedure in which groups of semantically like queries are named. The clustering procedure uses the content of historical preferences of users registered in the query log of the search engine. The method not only discloses the related queries, but also ranks them agreeing to a relevance criterion. Finally, we show with experiments over the query log of a search engine the potency of the method.