Fuzzy search result aggregation using Analytical Hierarchy Process

Abstract

A metasearch engines is a search engine that can be used to query multiple search engines at the same time. Typically a metasearch engine passes a user query to some other search engines, which in turn returns results in the form of ranked result lists. The metasearch engine then aggregates results returned by the search engines into a single ranked result list. Result aggregation is a well studied topic. In this paper we propose a comprehensive model for result merging t-norm Importance Guided Fuzzy Hybrid model (tIGFHM) that considers search engine prior performances during aggregation, uses Saaty's Analytic Hierarchy Process (AHP) to do pair wise comparisons of document and search engines and Yager's t-norm Importance Guided OWA operator to do final result aggregation. Our experiments show that our model performs better than conventional result merging models.