MUADDIB: A distributed recommender system supporting device adaptivity
ACM Transactions on Information Systems (TOIS)
User interests modeling based on multi-source personal information fusion and semantic reasoning
AMT'11 Proceedings of the 7th international conference on Active media technology
Hierarchical user interest modeling for Chinese web pages
Proceedings of the Third International Conference on Internet Multimedia Computing and Service
A methodology for mining document-enriched heterogeneous information networks
DS'11 Proceedings of the 14th international conference on Discovery science
Formation conditions of mutual adaptation in human-agent collaborative interaction
Applied Intelligence
Group topic modeling for academic knowledge discovery
Applied Intelligence
Ontology-based user profile learning
Applied Intelligence
Enhancing meta-portals using dynamic user context personalization techniques
Journal of Network and Computer Applications
Personalized news recommendation: a review and an experimental investigation
Journal of Computer Science and Technology - Special issue on Community Analysis and Information Recommendation
Representing and Reasoning About XML with Ontologies
Applied Intelligence
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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.