Measures of uncertainty in expert systems
Artificial Intelligence
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Improving the Quality of the Personalized Electronic Program Guide
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Ontological user profiling in recommender systems
ACM Transactions on Information Systems (TOIS)
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WWW '05 Proceedings of the 14th international conference on World Wide Web
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WWW '05 Special interest tracks and posters of the 14th international conference on World Wide Web
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Computer Communications
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AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
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CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
How do we measure and improve the quality of a hierarchical ontology?
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APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Expert Systems with Applications: An International Journal
Folksonomized ontology and the 3E steps technique to support ontology evolvement
Web Semantics: Science, Services and Agents on the World Wide Web
Information Systems Frontiers
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We consider recommender systems that filter information and only show the most preferred items. Good recommendations can be provided only when an accurate model of the user's preferences is available. We propose a novel technique for filling in missing elements of a user's preference model using the knowledge captured in an ontology. Furthermore, we show through experiments on the MovieLens data set that our model achieves a high prediction accuracy and personalization level when little about the user's preferences is known.