Web Query Classification and URL Ranking Based On Click through Approach?

In a web based application; different users may have different search goals when they submit it to a search engine. For a broad-topic and ambiguous query it is difficult. Here we propose a novel approach to infer user search goals by analyzing search engine query logs. This is typically shown in cases such as these: Different users have different backgrounds and interests. However, effective personalization cannot be achieved without accurate user profiles. We propose a framework that enables large-scale evaluation of personalized search. The goal of personalized IR (information retrieval) is to return search results that better match the user intent. First, we propose a framework to discover different user search goals for a query by clustering the proposed feedback sessions. Feedback sessions are getting constructed from user click-through logs and can efficiently reflect the information needs of users. Second, we propose an approach to generate pseudo-documents to better represent the feedback sessions for clustering. Most document-based methods focus on analyzing users’ clicking and browsing behaviors recorded at the users’ clickthrough data. In the Web search engines, clickthrough data are important implicit feedback mechanism from users. The bolded documents that have been clicked by the user have been ranked. Several personalized systems that employ clickthrough data to capture users’ interest have been proposed.