A learning algorithm for metasearching using rough set theory

Abstract

Metasearching is the process of combining search results of different search systems into a single set of ranked results, which is expected to be better than results of best of the participating search systems. In this paper, we present a supervised learning algorithm for metasearching. Our algorithm learns the ranking rules on the basis of user feedback based metasearching for the queries in the training set. We use rough set theory to mine the ranking rules. The ranking rules are validated using cross validation. The best of the ranking rules is then used to estimate the results of metasearching for the other queries. We compare our method with modified Shimura technique. We claim that our method is more useful than modified Shimura technique as it models userpsilas preference.