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

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    Author
    Chau, Michael
    Zeng, Daniel
    Chen, Hsinchun
    Huang, Michael
    Hendriawan, David
    Issue Date
    2003-04
    Submitted date
    2004-08-16
    Keywords
    Web Mining
    Internet
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial Intelligence lab
    AI lab
    Web searching
    Web content mining
    Collaborative information retrieval
    Collaboration behavior
    Collaborative filtering
    Multiagent
    Systems
    Software agents
    Post-retrieval analysis
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    Citation
    Design and evaluation of a multi-agent collaborative Web mining system 2003-04, 35(1):167-183 Decision Support Systems
    Publisher
    Elsevier
    Journal
    Decision Support Systems
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105861
    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 subjectsâ 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 usersâ 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.
    Type
    Journal Article (Paginated)
    Language
    en
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