Personalized search engines on mining user preferences using clickthrough data

In an 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. A major deficiency of generic search engines is that they follow the “one size fits all” model and are not adaptable to individual users. 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 address the problem of learning the user profile within the user\´s ongoing behaviors by using the user search. We propose a framework that enables large-scale evaluation of personalized search. User interest is employed in the clustering process to achieve personalization effect. 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 get 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. An example of clickthrough data for the query "apple," which contains a list of ranked search results presented to the user, which contains identification on the results that was previously clicked by the user. 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 bee- proposed.