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Learning Subjective Relevance to Facilitate Information Access.

James R. Chen, Nathalie Mathe: Learning Subjective Relevance to Facilitate Information Access. CIKM 1995: 218-225
@inproceedings{DBLP:conf/cikm/ChenM95,
  author    = {James R. Chen and
               Nathalie Mathe},
  title     = {Learning Subjective Relevance to Facilitate Information Access},
  booktitle = {CIKM '95, Proceedings of the 1995 International Conference on
               Information and Knowledge Management, November 28 - December
               2, 1995, Baltimore, Maryland, USA},
  publisher = {ACM},
  year      = {1995},
  pages     = {218-225},
  ee        = {db/conf/cikm/ChenM95.html, http://doi.acm.org/10.1145/221270.221574},
  crossref  = {DBLP:conf/cikm/95},
  bibsource = {DBLP, http://dblp.uni-trier.de}
}
BibTeX

Abstract

As the amount of available electronic information is dramatically increasing, the ability for rapid and effective access to information has become critical. Most traditional information access methods rely on measures of relevance based on information content. We propose a new approach which augments existing information access methods with subjective relevance learned from user feedback. We developed an adaptive system which helps users access information by employing learned knowledge about which documents are likely to be relevant, given the current user s information need and user profile. This system is based on a model, called a relevance network, which learns and generalizes relevance information in a rapid, cost-effective, and incremental manner. We present the design of the relevance network and results of experimental evaluation.

Copyright © 1995 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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CIKM '95, Proceedings of the 1995 International Conference on Information and Knowledge Management, November 28 - December 2, 1995, Baltimore, Maryland, USA. ACM 1995
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