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This paper proposes and evaluates 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, the paper does not advocate for replacement of multiple engines with an aggregate one, but rather facilitate simultaneous use of individual engines. This paper describes a machine learning approach to supporting switching between search engines and demonstrates its viability at tolerable interruption levels.
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