Extracting user interests from bookmarks on the web

Authors:
Jason J. Jung;Geun-Sik Jo
Affiliations:
School of Computer Engineering, Inha University, Incheon, Korea;School of Computer Engineering, Inha University, Incheon, Korea
Venue:
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Year:
2003

Citing 3
Cited 0

Probabilistic reasoning in intelligent systems: networks of plausible inference

Probabilistic reasoning in intelligent systems: networks of plausible inference
Modern Information Retrieval

Modern Information Retrieval
Collaborative information filtering by using categorized bookmarks on the web

INAP'01 Proceedings of the Applications of prolog 14th international conference on Web knowledge management and decision support

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Abstract

This paper regards bookmarking as the most important information to extract user preferences among user behaviors. Bookmarks are categorized on Bayesian networks by an ontology. Considering the relationships between categories, evidential supports are mutually propagated to improve the coverage of the potential preferences. Consequently, we have attempted to define bookmarking behaviors and apply them to the weight updating on users' preference map. We have measured the causal rate in order to improve accuracy of evidential supports and retrieved relational information between the behavioral patterns and user preferences throught temporally analyzing these patterns. For experiments, we made a dataset organized as 2718 bookmarks and had monitored 12 users' behaviors for 30 days.