Exploration of a heuristic approach to threshold learning in adaptive filtering (poster session)

  • Authors:

  • Chengxiang Zhai;Peter Jansen;David A. Evans

  • Affiliations:

  • Claritech Corporation, 5301 Fifth Ave., Pittsburgh, PA;Claritech Corporation, 5301 Fifth Ave., Pittsburgh, PA;Claritech Corporation, 5301 Fifth Ave., Pittsburgh, PA

  • Venue:
  • SIGIR '00 Proceedings of the 23rd annual international ACM SIGIR conference on Research and development in information retrieval
  • Year:
  • 2000

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Abstract

In this paper we examine the learning behavior of a heuristic threshold setting approach to information filtering. In particular, we study how different initial threshold settings and different updating parameter settings affect threshold learning. The results on one of the TREC news databases indicate that (1) learning allows recovery from the inevitable non-optimality of the initial conditions, and (2) a greater “willingness to learn” (expressed by a deliberate lowering of the score threshold in the learning stage) does eventually lead to a higher performance in spite of the expected initial performance penalty.