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Adaptive and Incremental Query Expansion for Cluster-based Browsing
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Koji Eguchi,
Hidetaka Ito,
Akira Kumamoto, and
Yakichi Kanata
View Paper (PDF)
Return to Session 1A: World Wide Web
In this
paper, we propose a new method of information retrieval which combines adaptive
and incremental query expansion with cluster-based browsing. The proposed
method attempts to accurately learn users' interests from their relevance
judgments on clustered search results instead of individual documents, reducing
users' loads for the judgments. The use of adaptive relevance feedback leads to
the capability for tracking vague or dynamically shifting goals of users.
Incrementally expanded and refined queries can be used in re-searching to
improve the retrieval effectiveness. We apply the proposed method to the
information retrieval on the World Wide Web and demonstrate its effectiveness
through basic experiments.
Copyright(C) 2000 ACM
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