SUSHI: Scoring Scaled Samples for Server Selection
Modern techniques for distributed information retrieval use a set of documents sampled from each server, but these samples have been underutilised in server selection. We describe a new server selection algorithm, SUSHI, which unlike earlier algorithms can make full use of the text of each sampled document and which does not need training data. SUSHI can directly optimise for many common cases, including high precision retrieval, and by including a simple stopping condition can do so while...[Show more]
|Collections||ANU Research Publications|
|Source:||Proceedings of Annual International ACM SIGIR Conference on Research and Development in Information Retrieval|
|01_Thomas_SUSHI:_Scoring_Scaled_Samples_2009.pdf||362.2 kB||Adobe PDF||Request a copy|
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