Query expansion based on relevance feedback and latent semantic analysis

Web search engines are one of the most popular tools on the Internet, which are widely used by experienced and inexperienced users. Constructing an adequate query, which represents the best specification of users’ information need to the search engine is an important concern of web users. Query expansion is a way to reduce this concern and increase user satisfaction. In this paper, a new method of query expansion is introduced. This method, which is a combination of relevant feedback and latent semantic analysis, finds the relative terms to the topics of user original query based on relevant documents selected by the user in relevant feedback step. The method is evaluated and compared with the Rocchio relevant feedback. The results indicate the capability of the method to better representation of user’s information need and increasing significantly user satisfaction

Query expansion based on relevance feedback and latent semantic analysis Keywords:

Query expansion , latent semantic analysis , relevant feedback