Web Metasearch as Belief Aggregation

Sergio A. Alvarez

Web metasearch requires a mechanism for combining rank-ordered lists of ratings returned by multiple search engines in response to a given user query. We view this as being analogous to the need for combining degrees of belief in probabilistic and uncertain reasoning in artificial intelligence. This paper describes a practical method for performing web metasearch based on a novel transformationbased theory of belief aggregation. The consensus ratings produced by this method take into account the item ratings/rankings output by individual search engines as well as the user’s preferences.

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