Learning implicit user interest hierarchy for context in personalization

被引:38
作者
Kim, Hyoung-Rae [1 ]
Chan, Philip K. [2 ]
机构
[1] Korea Employment Informat Serv, Informat Strategy Team, Seoul, South Korea
[2] Florida Inst Technol, Dept Comp Sci, Melbourne, FL 32901 USA
关键词
clustering algorithm; correlation function; user interest hierarchy; user modeling; user profile;
D O I
10.1007/s10489-007-0056-0
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
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.
引用
收藏
页码:153 / 166
页数:14
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