Personalization on the Net using Web mining: introduction
Communications of the ACM
Automatic personalization based on Web usage mining
Communications of the ACM
Ontology Learning and Its Application to Automated Terminology Translation
IEEE Intelligent Systems
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Web usage mining: discovery and applications of usage patterns from Web data
ACM SIGKDD Explorations Newsletter
IEEE Transactions on Knowledge and Data Engineering
Dogear: Social bookmarking in the enterprise
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
Ontologies are us: A unified model of social networks and semantics
Web Semantics: Science, Services and Agents on the World Wide Web
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Can all tags be used for search?
Proceedings of the 17th ACM conference on Information and knowledge management
Social tags: meaning and suggestions
Proceedings of the 17th ACM conference on Information and knowledge management
Tag recommendations in social bookmarking systems
AI Communications
An Integrated Approach to Extracting Ontological Structures from Folksonomies
ESWC 2009 Heraklion Proceedings of the 6th European Semantic Web Conference on The Semantic Web: Research and Applications
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Recommending New Tags Using Domain-Ontologies
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Towards ontology learning from folksonomies
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Connecting users and items with weighted tags for personalized item recommendations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Ontology emergence from folksonomies
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Bridging Folksonomies and Domain Ontologies: Getting Out Non-taxonomic Relations
ICDMW '10 Proceedings of the 2010 IEEE International Conference on Data Mining Workshops
The Knowledge Engineering Review
Ontology Mapping and Reasoning in Semantic Time Series Processing
Proceedings of International Conference on Information Integration and Web-based Applications & Services
Tag recommendation for open source software
Frontiers of Computer Science: Selected Publications from Chinese Universities
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Due to the explosive growth of the Web, the domain of Web personalization has gained great momentum both in the research and commercial areas. One of the most popular web personalization systems is recommender systems. In recommender systems choosing user information that can be used to profile users is very crucial for user profiling. In Web 2.0, one facility that can help users organize Web resources of their interest is user tagging systems. Exploring user tagging behavior provides a promising way for understanding users' information needs since tags are given directly by users. However, free and relatively uncontrolled vocabulary makes the user self-defined tags lack of standardization and semantic ambiguity. Also, the relationships among tags need to be explored since there are rich relationships among tags which could provide valuable information for us to better understand users. In this paper, we propose a novel approach for learning tag ontology based on the widely used lexical database WordNet for capturing the semantics and the structural relationships of tags. We present personalization strategies to disambiguate the semantics of tags by combining the opinion of WordNet lexicographers and users' tagging behavior together. To personalize further, clustering of users is performed to generate a more accurate ontology for a particular group of users. In order to evaluate the usefulness of the tag ontology, we use the tag ontology in a pilot tag recommendation experiment for improving the recommendation performance by exploiting the semantic information in the tag ontology. The initial result shows that the personalized information has improved the accuracy of the tag recommendation.