Metasearching using modified rough set based rank aggregation

A metasearch engine is a search engine, which uses the results of several search engines to produce a collated list of search results. The actual success of a meta-search engine directly depends on the aggregation technique underlying it. User satisfaction is obviously the most important factor in measuring the quality of search results. Therefore, we propose a metasearch system that models user feedback based metasearching. Our system uses the modified rough set based rank aggregation for metasearching. In the modified rough set based rank aggregation technique, we incorporate the confidence of the rules in predicting a class for a given set of data. We associate a score variable to the predicted class of the record, where the value of the variable is equal to the confidence measure of the rule. For each query in the training set, we mine the ranking rules using rough set theory and select the best rules-set by performing cross-validation test. Once the system is trained, we may use the best rule set to get the overall ranking for the results returned from different search systems in response to other queries. Previously modified rough set based rank aggregation technique has been already used for the performance evaluation of Web Search systems but here, we apply this method for meta searching. We also perform an evaluation of our proposed system by three independent evaluators. We compare our method with metasearching method based on rough set based rank aggregation and find that our method performs better than the metasearching method based on rough set based rank aggregation.