Enhancing Web Search by Promoting Multiple Search Engine Use

Abstract

Any given Web search engine may provide higher quality results than others for certain queries. Therefore, it is in users’ best interest to utilize multiple search engines. In this paper, we propose and evaluate a framework that maximizes users’ search effectiveness by directing them to the engine that yields the best results for the current query. In contrast to prior work on meta-search, we do not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. We describe a machine learning approach to supporting switching between search engines and demonstrate its viability at tolerable interruption levels. Our findings have implications for fluid competition between search engines.