Importance

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Abstract

A computer-implemented method for facilitating World Wide Web Searches and like database searches by combining search result documents, as provided by separate search engines in response to a query, into one single integrated list so as to produce a single document with a ranked list of pages, by forming a set of selected queries, the queries including respective terms, for which selected queries relevance data from past data is known, herein referred to as training queries, in a vector space comprising all training queries, the relevance data comprising judgments by a user as to whether a page is appropriate for a query which retrieved it. Further steps in the method are identifying a set of k most similar training queries to current query q, computing an average relevant document distribution of the k queries within the training queries' search results for each of the search engines, using the computed relevant document distributions, finding an optimal number of pages to select from the result set of each search engine when N total pages are to be retrieved, and creating a final retrieved set by forming the union of the top .lambda..sub.s pages from each search engine.

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First Claim