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<p><b>Abstract</b>—The World Wide Web is a world of great richness, but finding information on the Web is also a great challenge. Keyword-based querying has been an immediate and efficient way to specify and retrieve related information that the user inquires. However, conventional document ranking based on an automatic assessment of document relevance to the query may not be the best approach when little information is given, as in most cases. In order to clarify the ambiguity of the short queries given by users, we propose the idea of <it>concept-based relevance feedback</it> for Web information retrieval. The idea is to have users give two to three times more feedback in the same amount of time that would be required to give feedback for conventional feedback mechanisms. Under this design principle, we apply clustering techniques to the initial search results to provide concept-based browsing. We show the performances of various feedback interface designs and compare their pros and cons. We shall measure <it>precision</it> and <it>relative recall</it> to show how clustering improves performance over conventional similarity ranking and, most importantly, we shall show how the assistance of concept-based presentation reduces browsing labor.</p>
Query expansion, relevance feedback, concept-based feedback, keyword extraction, document clustering, document-based browsing, cluster-based browsing.

C. Chang and C. Hsu, "Enabling Concept-Based Relevance Feedback for Information Retrieval on the WWW," in IEEE Transactions on Knowledge & Data Engineering, vol. 11, no. , pp. 595-609, 1999.
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