WebFusion: Fundamentals and Principals of a Novel Meta Search Engine
Amir Hosein Keyhanipour, Behzad Moshiri, Maryam Piroozmand, Carrie L. Lucas · The 2006 IEEE International Joint Conference on Neural Network Proceedings · 2006
Several contemporary commercial search engines and meta-search engines attempt to produce more relevant results for a particular query intelligently. In this paper, a novel meta-search engine, named as WebFusion, has been introduced. This meta-search engine learns the expertness of each underlying search engine in a certain category based on the users' preferences. Moreover, an intelligent re-ranking method is proposed based on OWA. This re-ranking method is used to fuse the results' scores of the underlying search engines. WebFusion uses the click-through data concept to give a content-oriented ranking score to each result page. Click-through data concept is the implicit feedback of the users' preferences, which is also used as a reinforcement signal in the learning process, to predict the users' preferences and reduces the seeking time in the search result list. This research provides a direct mapping between users' categories and the underlying search engines, based on users' judgments. Experimental results showed that the average click rate and the variance of clicks are noticeably decreased comparing with ProFusion while the relevancy of the responses is increased.