Different users have different search goals when they submit a query to a search engine. In this paper we aim at discovering the number of diverse user's search goal for giving a query and for each goal a keyword is associated automatically. We initially derive user's search goal for a query by clustering our proposed feedback conclave. Then the feedback conclave is mapped to pseudo-documents so that the user's needs are retrieved efficiently. Finally, these pseudo documents are then clustered to deduce user search goals and depict them with some keywords. Though K means clustering is used in the existing system sometimes queries may not exactly represent user specific information needs. This method only finds whether a pair of query is belonging to the same set of goal and does not look into goal in detail. Hence we put forward a fuzzy similarity-based self-constructing algorithm for feature clustering. Our method works efficiently and will return provide better inferred properties than any other method.
分享
引用
