TreeViz: treemap visualization of hierarchically structured information
CHI '92 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the tenth annual conference on Object-oriented programming systems, languages, and applications
Social information filtering: algorithms for automating “word of mouth”
CHI '95 Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
GroupLens: applying collaborative filtering to Usenet news
Communications of the ACM
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Architectural styles and the design of network-based software architectures
Architectural styles and the design of network-based software architectures
Manipulation, analysis and retrieval systems for audio signals
Manipulation, analysis and retrieval systems for audio signals
Automatic Feature Extraction for Classifying Audio Data
Machine Learning
An innovative three-dimensional user interface for exploring music collections enriched
MULTIMEDIA '06 Proceedings of the 14th annual ACM international conference on Multimedia
The Description Logic Handbook
The Description Logic Handbook
A new approach to hierarchical clustering and structuring of data with Self-Organizing Maps
Intelligent Data Analysis
Localized alternative cluster ensembles for collaborative structuring
ECML'06 Proceedings of the 17th European conference on Machine Learning
Content-based audio classification and retrieval by support vector machines
IEEE Transactions on Neural Networks
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Organizing multimedia data is very challenging. One of the most important approaches to support users in searching and navigating media collections is collaborative filtering. Recently, systems as flickr or last.fm have become popular. They allow users to not only rate but also tag items with arbitrary labels. Such systems replace the concept of a global common ontology, as envisioned by the Semantic Web, with a paradigm of heterogeneous, local "folksonomies". The problem of such tagging systems is, however, that resulting taggings carry only little semantics. In this paper, we present an extension to the tagging approach. We allow tags to be grouped into aspects. We show that introducing aspects does not only help the user to manage large numbers of tags, but also facilitates data mining in various ways. We exemplify our approach on Nemoz, a distributed media organizer based on tagging and distributed data mining.