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Incremental Relevance Feedback for Information Filtering.

James Allan: Incremental Relevance Feedback for Information Filtering. SIGIR 1996: 270-278
@inproceedings{DBLP:conf/sigir/Allan96,
  author    = {James Allan},
  title     = {Incremental Relevance Feedback for Information Filtering},
  booktitle = {SIGIR},
  year      = {1996},
  pages     = {270-278},
  ee        = {db/conf/sigir/Allan96.html},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}

Abstract

We use data from the TREC routing experiments to explore how relevance feedback can be applied incrementally - using a few judged documents each time - to achieve results that are as good as if the feedback occurred in one pass. We show that relatively few judgments are needed to get high-quality results. We also demonstrate methods that reduce the amount of information archived from past judged documents without adversely affecting effectiveness. A novel simulation shows that such techniques are useful for handling long-standing queries with drifting notions of relevance.

Copyright © 1996 by the ACM, Inc., used by permission. Permission to make digital or hard copies is granted provided that copies are not made or distributed for profit or direct commercial advantage, and that copies show this notice on the first page or initial screen of a display along with the full citation.


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Hans-Peter Frei, Donna Harman, Peter Schäuble, Ross Wilkinson (Eds.): Proceedings of the 19th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, SIGIR'96, August 18-22, 1996, Zurich, Switzerland (Special Issue of the SIGIR Forum). ACM 1996, ISBN 0-89791-792-8
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