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Threshold setting in adaptive filtering

Threshold setting in adaptive filtering A major problem in using current best‐match methods in a filtering task is that of setting appropriate thresholds, which are required in order to force a binary decision on notifying a user of a document. We discuss methods for setting such thresholds and adapting them as a result of feedback information on the performance of the profile. These methods fit within the probabilistic approach to retrieval, and are applied to a probabilistic system. Some experiments, within the framework of the TREC‐7 adaptive filtering track, are described. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Documentation Emerald Publishing

Threshold setting in adaptive filtering

Journal of Documentation , Volume 56 (3): 20 – Jun 1, 2000

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References (12)

Publisher
Emerald Publishing
Copyright
Copyright © 2000 MCB UP Ltd. All rights reserved.
ISSN
0022-0418
DOI
10.1108/EUM0000000007118
Publisher site
See Article on Publisher Site

Abstract

A major problem in using current best‐match methods in a filtering task is that of setting appropriate thresholds, which are required in order to force a binary decision on notifying a user of a document. We discuss methods for setting such thresholds and adapting them as a result of feedback information on the performance of the profile. These methods fit within the probabilistic approach to retrieval, and are applied to a probabilistic system. Some experiments, within the framework of the TREC‐7 adaptive filtering track, are described.

Journal

Journal of DocumentationEmerald Publishing

Published: Jun 1, 2000

Keywords: Document management; Binary logic; Flexibility

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