QueryFind: search ranking based on users' feedback and expert's agreement

Abstract

A novel ranking method named as QueryFind, based on learning from historical query logs, is proposed to predict users' information needs and reduce the seeking time from the search result list. Our method uses not only the users' feedback but also the recommendation of a source search engine. Based on this ranking method, we utilize users' feedback to evaluate the quality of Web pages implicitly. We also apply the meta-search concept to give each Web page a content-oriented ranking score. Therefore, the time users spend for seeking out their required information from search result list can be reduced and the more relevant Web pages can be presented. We also propose a novel evaluation criterion to verify the feasibility of our ranking method. The criterion is to capture the ranking order of Web pages that users have clicked from the search result list. Finally, our experiments show that the time users spend on seeking out their required information can be reduced significantly.