A vector space model for automatic indexing
Communications of the ACM
Information Storage and Retrieval Systems: Theory and Implementation
Information Storage and Retrieval Systems: Theory and Implementation
User Profile Modeling and Applications to Digital Libraries
ECDL '99 Proceedings of the Third European Conference on Research and Advanced Technology for Digital Libraries
Usage patterns of collaborative tagging systems
Journal of Information Science
Network properties of folksonomies
AI Communications - Network Analysis in Natural Sciences and Engineering
Exploring folksonomy for personalized search
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Personalization of tagging systems
Information Processing and Management: an International Journal
Building users' profiles from clustering resources in collaborative tagging systems
AMT'10 Proceedings of the 6th international conference on Active media technology
Ontologies are us: a unified model of social networks and semantics
ISWC'05 Proceedings of the 4th international conference on The Semantic Web
Social linkage and ranking model for tags-based resources
International Journal of Metadata, Semantics and Ontologies
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The 'Collaborative Tagging' is gaining popularity on Web 2.0, this new generation of Web which makes user reader/writer. The 'Tagging' is a mean for users to express themselves freely through additions of label called 'Tags' to shared resources. One of the problems encountered in current tagging systems is to define the most appropriate tag for a resource. Tags are typically listed in order of popularity, as del-icio-us. But the popularity of the tag does not always reflect its importance and representativeness for the resource to which it is associated. Starting from the assumptions that the same tag for a resource can take different meanings for different users, and a tag from a knowledgeable user would be more important than a tag from a novice user, we propose an approach for weighting resource's tags based on user profile. For this we define a user model for his integration in the tag weight calculation and a formula for this calculation, based on three factors namely the user, the degree of approximation between his interest centers and the resource field, expertise and personal assessment for tags associated to the resource. A resource descriptor containing the best tags is created.