Distributed multi-agent information filtering—A comparative study: Research Articles

Authors:
S. Mukhopadhyay;S. Peng;R. Raje;J. Mostafa;M. Palakal
Affiliations:
Computer and Information Science, Indiana University Purdue University at Indianapolis, 723 West Michigan Street SL280, Indianapolis, IN 46202;Computer and Information Science, Indiana University Purdue University at Indianapolis, 723 West Michigan Street SL280, Indianapolis, IN 46202;Computer and Information Science, Indiana University Purdue University at Indianapolis, 723 West Michigan Street SL280, Indianapolis, IN 46202;School of Library and Information Science and School of Informatics, Indiana University, Bloomington, IN 47402;Computer and Information Science, Indiana University Purdue University at Indianapolis, 723 West Michigan Street SL280, Indianapolis, IN 46202
Venue:
Journal of the American Society for Information Science and Technology
Year:
2005

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Abstract

Information filtering is a technique to identify, in large collections, information that is relevant according to some criteria (e.g., a user's personal interests, or a research project objective). As such, it is a key technology for providing efficient user services in any large-scale information infrastructure, e.g., digital libraries. To provide large-scale information filtering services, both computational and knowledge management issues need to be addressed. A centralized (single-agent) approach to information filtering suffers from serious drawbacks in terms of speed, accuracy, and economic considerations, and becomes unrealistic even for medium-scale applications. In this article, we discuss two distributed (multi-agent) information filtering approaches, that are distributed with respect to knowledge or functionality, to overcome the limitations of single-agent centralized information filtering. Large-scale experimental studies involving the well-known TREC data set are also presented to illustrate the advantages of distributed filtering as well as to compare the different distributed approaches. © 2005 Wiley Periodicals, Inc.