Mining Unexpected Web Usage Behaviors
ICDM '08 Proceedings of the 8th industrial conference on Advances in Data Mining: Medical Applications, E-Commerce, Marketing, and Theoretical Aspects
Developing an ontology-supported information integration and recommendation system for scholars
Expert Systems with Applications: An International Journal
A framework for mining meaningful usage patterns within a semantically enhanced web portal
Proceedings of the Third C* Conference on Computer Science and Software Engineering
WebUser: mining unexpected web usage
International Journal of Business Intelligence and Data Mining
A literature review and classification of recommender systems research
Expert Systems with Applications: An International Journal
Web page recommendation based on semantic web usage mining
SocInfo'12 Proceedings of the 4th international conference on Social Informatics
A Pattern Language for Knowledge Discovery in a Semantic Web context
International Journal of Information Technology and Web Engineering
Relational concept analysis: mining concept lattices from multi-relational data
Annals of Mathematics and Artificial Intelligence
Modeling contextual agreement in preferences
Proceedings of the 23rd international conference on World wide web
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Content adaptation on the Web reduces available information to a subset that matches a user's anticipated needs. Recommender systems rely on relevance scores for individual content items; in particular, pattern-based recommendation exploits co-occurrences of items in user sessions to ground any guesses about relevancy. To enhance the discovered patterns' quality, the authors propose using metadata about the content that they assume is stored in a domain ontology. Their approach comprises a dedicated pattern space built on top of the ontology, navigation primitives, mining methods, and recommendation techniques.