.png){kind=icon}
返回
Learning implicit user interest hierarchy for context in personalization
.png){kind=icon}
PRE
AI
DOI：10.1007/s10489-007-0056-0.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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.
Keyword:
clustering algorithm
correlation function
user interest hierarchy
user modeling
user profile
期刊
IF:
3.5
论文数:
7.6K
被引数:
1.7W
机构
引用论文
Learning and revising user profiles: The identification of interesting Web sites.png)
MACHINE LEARNING
MACHINE LEARNINGIF2.9
PREAI
分享收藏分享收藏分享收藏
查看更多内容