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

Michael Chau, Daniel Zeng, Hsinchun Chen, Michael Huang, David Hendriawan

Research output: Contribution to journalArticlepeer-review

74 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)167-183
Number of pages17
JournalDecision Support Systems
Issue number1
StatePublished - Apr 2003


  • Collaboration behavior
  • Collaborative filtering
  • Collaborative information retrieval
  • Multi-agent systems
  • Post-retrieval analysis
  • Software agents
  • Web content mining
  • Web searching

ASJC Scopus subject areas

  • Management Information Systems
  • Information Systems
  • Developmental and Educational Psychology
  • Arts and Humanities (miscellaneous)
  • Information Systems and Management


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