Aggregation of Results Based on OWA in Meta Search Engine
Fan Yin-chen · Microelectronics & Computer · 2007
Meta Search engines are proposed to increase the search coverage by combining several search engines. However, the problem of the information overload becomes more severe and most returned results are irrelevant to user’s interests. That affects the retrieval quality and increases the user cost. For settling this problem, an algorithm of results merging based on OWA is brought forward. It learns the expertness of the underlying search engines in a certain category based on the users' preferences. It also uses the feedback of the users' preferences to give a content-oriented ranking score to each result page. The decision lists of underling search engines have been fused using OWA approach and the application of optimistic operator as weight function has been investigated. Researches and experiments are carried out show the strategy can promotes the effectiveness and efficiency of retrieval.