Usage patterns of collaborative tagging systems
Journal of Information Science
Proceedings of the 15th international conference on World Wide Web
P-TAG: large scale automatic generation of personalized annotation tags for the web
Proceedings of the 16th international conference on World Wide Web
Real-time automatic tag recommendation
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Metric-based ontology learning
Proceedings of the 2nd international workshop on Ontologies and information systems for the semantic web
Attaching UI enhancements to websites with end users
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 18th international conference on World wide web
Evaluating similarity measures for emergent semantics of social tagging
Proceedings of the 18th international conference on World wide web
Learning to Rank for Information Retrieval
Foundations and Trends in Information Retrieval
Learning concept hierarchies from text corpora using formal concept analysis
Journal of Artificial Intelligence Research
LSA as ground truth for recommending "flickr-aware" representative tags
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
Learning compact hashing codes for efficient tag completion and prediction
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Social bookmarking sites typically visualize user-generated tags as tag clouds. While tag clouds effectively show the relative frequency and thus popularity of tags, they fail to convey two aspects to the users: (1) the similarity between tags, and (2) the abstractness of tags. We suggest an alternative to tag clouds known as tag hierarchies. Tag hierarchies are based on a minimum evolution-based greedy algorithm for tag hierarchy construction, which iteratively includes optimal tags into the tree that introduce minimum changes to the existing taxonomy. Our algorithm also uses a global tag ranking method to order tags according to their levels of abstractness as well as popularity such that more abstract tags will appear at higher levels in the taxonomy. Based on the tag hierarchy, we derive a new tag recommendation algorithm, which is a structure-based approach that does not require heavily trained models and thus is highly efficient. User studies and quantitative analysis suggest that (1) the tag hierarchy can potentially reduce the user's tagging time in comparison to tag clouds and other tag tree structures, and (2) the tag recommendation algorithm significantly outperforms existing content-based methods in quality.