A Ranking Algorithm Based on Topic Preference for Meta-search
Lixin Han · Computer Technology and Development · 2013
Meta-search engine launches query simultaneously to its member search engines and shows a combined and ranked results list.Compared with the traditional search engine,meta-search engine has a better recall rate.However with the large amount of return items from its member engines,the precision rate and MMR still need to be perfected.Each member engine performs differently in the searching tasks with different topics in view of precision rate and MMR.In this paper,present a topic preference based ranking algorithm.Using Beeferman clustering method divides the search topic,with Borda ranking algorithm classify and rank the entries obtained by meta-search engine based on topic,improving the meta-search query quality and enhancing user experience.