WebFusion: Fundamentals and Principals of a Novel Meta Search Engine

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.