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Learning implicit user interest hierarchy for context in personalization

Published:12 January 2003Publication History

ABSTRACT

To provide a more robust context for personalization, we desire to extract a continuum of general (long-term) to specific (short-term) interests of a user. Our proposed approach is to learn a user interest hierarchy (UIH) from a set of web pages visited by a user. We devise a divisive hierarchical clustering (DHC) algorithm to group words (topics) into a hierarchy where more general interests are represented by a larger set of words. Each web page can then be assigned to nodes in the hierarchy for further processing in learning and predicting interests. This approach is analogous to building a subject taxonomy for a library catalog system and assigning books to the taxonomy. Our approach does not need user involvement and learns the UIH "implicitly." Furthermore, it allows the original objects, web pages, to be assigned to multiple topics (nodes in the hierarchy). In this paper, we focus on learning the UIH from a set of visited pages. We propose a few similarity functions and dynamic threshold-finding methods, and evaluate the resulting hierarchies according to their meaningfulness and shape

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            cover image ACM Conferences
            IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces
            January 2003
            344 pages
            ISBN:1581135866
            DOI:10.1145/604045

            Copyright © 2003 ACM

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            New York, NY, United States

            Publication History

            • Published: 12 January 2003

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