Design and evaluation of a multi-agent collaborative Web mining system

  • Authors:

  • Michael Chau;Daniel Zeng;Hsinchun Chen;Michael Huang;David Hendriawan

  • Affiliations:

  • Department of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ;Department of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ;Department of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ;Department of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ;Department of Management Information Systems, Eller College of Business and Public Administration, The University of Arizona, Tucson, AZ

  • Venue:
  • Decision Support Systems - Web retrieval and mining
  • Year:
  • 2003

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Abstract

Most existing Web search tools work only with individual users and do not help a user benefit from previous search experiences of others. In this paper, we present the Collaborative Spider, a multi-agent system designed to provide post-retrieval analysis and enable across-user collaboration in Web search and mining. This system allows the user to annotate search sessions and share them with other users. We also report a user study designed to evaluate the effectiveness of this system. Our experimental findings show that subjects' search performance was degraded, compared to individual search scenarios in which users had no access to previous searches, when they had access to a limited number (e.g., 1 or 2) of earlier search sessions done by other users. However, search performance improved significantly when subjects had access to more search sessions. This indicates that gain from collaboration through collaborative Web searching and analysis does not outweigh the overhead of browsing and comprehending other users' past searches until a certain number of shared sessions have been reached. In this paper, we also catalog and analyze several different types of user collaboration behavior observed in the context of Web mining.