Unified utility maximization framework for resource selection

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Unified utility maximization framework for resource selection
This paper presents a unified utility framework for resource selection of distributed text information retrieval. This new framework shows an efficient and effective way to infer the probabilities of relevance of all the documents across the text databases. With the estimated relevance information, resource selection can be made by explicitly optimizing the goals of different applications. Specifically, when used for database recommendation, the selection is optimized for the goal of highrecall (include as many relevant documents as possible in the selected databases); when used for distributed document retrieval, the selection targets the high-precision goal (high precision in the final merged list of documents). This new model provides a more solid framework for distributed information retrieval. Empirical studies show that it is at least as effective as other state-of-the-art algorithms. Categories and Subject Descriptors H.3.3 [Information Search and Retrieval]: General Terms Algo...
Luo Si, James P. Callan
Added 01 Jul 2010
Updated 01 Jul 2010
Type Conference
Year 2004
Where CIKM
Authors Luo Si, James P. Callan
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