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Pseudo-relevance feedback in Web information retrieval using segments' subjective importance values

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2 Author(s)
Seung Yeol Yoo ; Sch. of Comput. Sci. & Eng., New South Wales Univ., Sydney, NSW, Australia ; A. Hoffmann

To make Web search more effective, we address the problem of articulating a user's information needs more effectively. This is done in an iterative way, by allowing the user to provide relevance feedback regarding individual segments of retrieved Web-pages. Previously applied methods are limited to discovering 'general importance values of segments' (based on the authors' 'objective views' i.e., main topics) rather than 'subjective importance values of segments' (based on a user's 'subjective view' i.e., personal information needs). In this paper, a user's interests are incrementally identified by allowing the user to iteratively select relevant keywords or phrases from a set of system-recommended candidate-keywords and candidate-phrases (i.e., pseudo-relevance feedback). It makes it possible to discover 'subjective importance values of segments' that can be dynamically changed by the user by indicating their interests regarding retrieved Web-pages. The important segments, selected by the user, provide higher precision of pseudo-relevance feedback for further Web information retrieval purposes.

Published in:

The 2005 IEEE/WIC/ACM International Conference on Web Intelligence (WI'05)

Date of Conference:

19-22 Sept. 2005