Threshold setting in adaptive filtering

Author:

Robertson Stephen,Walker Stephen

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.

Publisher

Emerald

Subject

Library and Information Sciences,Information Systems

Reference14 articles.

1. Journal of Documentation, 53(1), 1997. Includes: Robertson, S.E. Overview of the Okapi projects, 3-7.

2. Sparck Jones, K., Walker, S. and Robertson, S.E. A probabilistic model of information retrieval: development and status. University of Cambridge Computer Laboratory Technical Report No. 446,1998. http://www.cl.cam. ac.uk/ftp/papers/reports/#TR446 (visited 9 September 1999).

3. The seventh text REtrieval conference (TREC-7)

4. Learning while filtering documents

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