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Learning implicit user interest hierarchy for context in personalization

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Abstract

To provide a more robust context for personalization, we desire to extract a continuum of general to specific interests of a user, called a user interest hierarchy (UIH). The higher-level interests are more general, while the lower-level interests are more specific. A UIH can represent a user’s interests at different abstraction levels and can be learned from the contents (words/phrases) in a set of web pages bookmarked by a user. We propose a divisive hierarchical clustering (DHC) algorithm to group terms (topics) into a hierarchy where more general interests are represented by a larger set of terms. Our approach does not need user involvement and learns the UIH “implicitly”. To enrich features used in the UIH, we used phrases in addition to words. Our experiment indicates that DHC with the Augmented Expected Mutual Information (AEMI) correlation function and MaxChildren threshold-finding method built more meaningful UIHs than the other combinations on average; using words and phrases as features improved the quality of UIHs.

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Authors and Affiliations

  1. Korea Employment Information Service, Information Strategy Team, 77-11 Mullae-dong 3-ga, YeongDeungPo-gu, Seoul, South Korea

    Hyoung-Rae Kim

  2. Department of Computer Sciences, Florida Institute of Technology, 150 West University Blvd., Melbourne, FL, 32901, USA

    Philip K. Chan

Authors
  1. Hyoung-Rae Kim
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  2. Philip K. Chan
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Correspondence to Hyoung-Rae Kim.

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Kim, HR., Chan, P.K. Learning implicit user interest hierarchy for context in personalization. Appl Intell 28, 153–166 (2008). https://doi.org/10.1007/s10489-007-0056-0

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  • DOI: https://doi.org/10.1007/s10489-007-0056-0

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