Expert Agreement and Content Based Reranking in a Meta Search Environment Using Mearf

Abstract

Recent increase in the number of search engines on the Web and the availability of meta search engines that can query multiple search engines makes it important to find effective methods for combining results coming from different sources. In this paper we introduce novel methods for reranking in a meta search environment based on expert agreement and contents of the snippets. We also introduce an objective way of evaluating different methods for ranking search results. Our experimental evaluation shows that some of our methods produce rankings that are consistently better than the rankings produced by methods that are commonly used in many meta search engines as well as rankings produced by a popular search engine.