A Compressive Survey on Restructuring User Search Results by Using Feedback Session

this internet search engine relevance may be enhanced by means of considering end user search goal. In addition to the individual search engine optimization experience is usually increased through inferring individual search goals. This paper proposes a novel approach to infer user search goals by analyzing search engine query logs known as feedback session. First framework is proposed to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are constructed from user click-through logs efficiently. Second a novel approach to generate pseudo-documents to better represent the feedback sessions for clustering. In the proposed technique we are going to implement Semantic Clustering algorithm to improve the results as compare to other techniques. Finally, a new criterion “Classified Average Precision (CAP)” to evaluate the performance of inferring user search goals.