One-class svms for document classification
The Journal of Machine Learning Research
Estimating the Support of a High-Dimensional Distribution
Neural Computation
The Benefit of Using Tag-Based Profiles
LA-WEB '07 Proceedings of the 2007 Latin American Web Conference
Personalized recommendation in social tagging systems using hierarchical clustering
Proceedings of the 2008 ACM conference on Recommender systems
Folks in Folksonomies: social link prediction from shared metadata
Proceedings of the third ACM international conference on Web search and data mining
Social tagging in recommender systems: a survey of the state-of-the-art and possible extensions
Artificial Intelligence Review
Web search personalization via social bookmarking and tagging
ISWC'07/ASWC'07 Proceedings of the 6th international The semantic web and 2nd Asian conference on Asian semantic web conference
Information retrieval in folksonomies: search and ranking
ESWC'06 Proceedings of the 3rd European conference on The Semantic Web: research and applications
RED'10 Proceedings of the Third international conference on Resource Discovery
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Social tagging constitutes one of the defining characteristics of Web 2.0 as it allows users to collectively classify and find diverse resources, such as Web pages, songs or pictures, using open-ended tags. The data structures underlying these systems, also known as folksonomies, suffered an explosive growth on account of the widespread success of social tagging. Thus, it is becoming increasingly difficult for users to find interesting resources as well as filter information streams coming from this massive amount of user-generated content on Web 2.0. In addition, most resources lacks easily extractable content to apply traditional content-based profiling approaches. In this paper we present an approach to build tag-based profiles for multimedia resources (such as songs, pictures or videos) using the social tags associated to resources as a means to describe them and, in turn, user interests. Experimental results show that the tags assigned by members of the community can help to predict the interestigness of a given resource for a user in an effective way.