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SIGIR
2010
ACM

A joint probabilistic classification model for resource selection

11 years 2 days ago
A joint probabilistic classification model for resource selection
Resource selection is an important task in Federated Search to select a small number of most relevant information sources. Current resource selection algorithms such as GlOSS, CORI, ReDDE, Geometric Average and the recent classificationbased method focus on the evidence of individual information sources to determine the relevance of available sources. Current algorithms do not model the important relationship information among individual sources. For example, an information source tends to be relevant to a user query if it is similar to another source with high probability of being relevant. This paper proposes a joint probabilistic classification model for resource selection. The model estimates the probability of relevance of information sources in a joint manner by considering both the evidence of individual sources and their relationship. An extensive set of experiments have been conducted on several datasets to demonstrate the advantage of the proposed model. Categories and Subje...
Dzung Hong, Luo Si, Paul Bracke, Michael Witt, Tim
Added 06 Dec 2010
Updated 06 Dec 2010
Type Conference
Year 2010
Where SIGIR
Authors Dzung Hong, Luo Si, Paul Bracke, Michael Witt, Tim Juchcinski
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