Query modification using genetic algorithms in vector space models

Abstract

The ability of information retrieval system parameters on the basis of user's relevance feedback for improving the retrieval effectiveness has become an important and also an active research area. One such adjustment is to modify the query which reflects the user's information request via the judgment of the previously retrieved results. In this paper we present our idea, based on an adaptive technique of genetic algorithms, for modifying a user's query to improve the retrieval results. The effectiveness of query modification has been tested on the Cranfield Collection, a classical set of documents, queries and known relevance responses to each query. Results of this study show that the method is highly effective, achieving substantially higher precision levels at each fixed recall level than those have been reported by other research groups.