Usage patterns of collaborative tagging systems
Journal of Information Science
HT06, tagging paper, taxonomy, Flickr, academic article, to read
Proceedings of the seventeenth conference on Hypertext and hypermedia
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Flickr tag recommendation based on collective knowledge
Proceedings of the 17th international conference on World Wide Web
Tag Recommendations in Folksonomies
PKDD 2007 Proceedings of the 11th European conference on Principles and Practice of Knowledge Discovery in Databases
Personalized, interactive tag recommendation for flickr
Proceedings of the 2008 ACM conference on Recommender systems
Collaborative resource discovery in social tagging systems
Proceedings of the 18th ACM conference on Information and knowledge management
CubeLSI: An effective and efficient method for searching resources in social tagging systems
ICDE '11 Proceedings of the 2011 IEEE 27th International Conference on Data Engineering
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We have been witnessing an increasing number of social tagging systems on the web. Tags help users understand a resource readily and accurately. In a social tagging system, however, there are typically a fairly large number of resources each associated with a long list of tags. When browsing resources, users are reluctant to read these tags one by one. Instead, users prefer a shorter list of tags as a compact description of a resource. Such a tag description facilitates users to understand the resource accurately and effortlessly. This calls for a generator for a tag description, which selects a set of high-quality tags for a given resource. The tag description condenses the original tag list by retaining the most important tags of the long list. We propose that a good generator should go beyond pure tag popularity and towards diversifying a tag description. In this paper, we present a general framework of selecting a set of k tags as the description for a given resource. In addition, a generative model BTM is proposed to model users' tagging process. The experimental results on real-world tagging data confirm the effectiveness of the proposed approach in social tagging systems, showing significant improvement over the other baselines.