Effective ranking fusion methods for personalized metasearch engines

Abstract

Metasearch engines are a significant part of the information retrieval process. Most of Web users use them directly or indirectly to access information from more than one data sources. The cornerstone of their technology is their rank aggregation method, which is the algorithm they use to classify the collected results. In this paper we present three new rank aggregation methods. At first, we propose a method that takes into consideration the regional data for the user and the pages and assigns scores according to a variety of user defined parameters. In the second expansion, not all component engines are treated equally. The user is free to define the importance of each engine by setting appropriate weights. The third algorithm is designed to classify pages having URLs that contain subdomains. The three presented methods are combined into a single, personalized scoring formula, the Global KE. All algorithms have been implemented in QuadSearch, an experimental metasearch engine available at http://quadsearch.csd.auth.gr. © 2008 IEEE.