Inferring User Search Goals Engine
B Agale Deepali · 2014
Different users may have different search goals when they submit broad-topic and ambiguous query, to a search engine. The inference and analysis of user search goals can be very useful in improving performance of search engine. To infer user search goals by analyzing search engine query logs a novel approach is proposed. First, we propose a framework to find out different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are built from user click-through data and can efficiently reflect the information needs of users. Second, then propose a novel approach to generate pseudo-documents by using feedback sessions for clustering. For clustering we use a new algorithm which is bisecting K-means algorithm. At the end, a new criterion “Classified Average Precision (CAP)” is proposed to evaluate the performance of inferring user search goals.