Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
Scatter/Gather: a cluster-based approach to browsing large document collections
SIGIR '92 Proceedings of the 15th annual international ACM SIGIR conference on Research and development in information retrieval
The effect of adding relevance information in a relevance feedback environment
SIGIR '94 Proceedings of the 17th annual international ACM SIGIR conference on Research and development in information retrieval
A case for interaction: a study of interactive information retrieval behavior and effectiveness
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Reexamining the cluster hypothesis: scatter/gather on retrieval results
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Search and Ranking Algorithms for Locating Resources on the World Wide Web
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Proceedings of the 22nd annual international ACM SIGIR conference on Research and development in information retrieval
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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.