Fab: content-based, collaborative recommendation
Communications of the ACM
BAYES AND EMPIRICAL BAYES METHODS FOR DATA ANALYSIS
Statistics and Computing
Learning to Perform Moderation in Online Forums
WI '03 Proceedings of the 2003 IEEE/WIC International Conference on Web Intelligence
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
Slash(dot) and burn: distributed moderation in a large online conversation space
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
How oversight improves member-maintained communities
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Social Bookmarking in the Enterprise
Queue - Social Computing
Usage patterns of collaborative tagging systems
Journal of Information Science
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Training linear SVMs in linear time
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Why we tag: motivations for annotation in mobile and online media
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Proceedings of the 2007 international ACM conference on Supporting group work
Automatically assessing the post quality in online discussions on software
ACL '07 Proceedings of the 45th Annual Meeting of the ACL on Interactive Poster and Demonstration Sessions
Tagommenders: connecting users to items through tags
Proceedings of the 18th international conference on World wide web
Connecting users and items with weighted tags for personalized item recommendations
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Improving the accuracy of tagging recommender system by using classification
ICACT'10 Proceedings of the 12th international conference on Advanced communication technology
Tag expression: tagging with feeling
UIST '10 Proceedings of the 23nd annual ACM symposium on User interface software and technology
Personalized recommender system based on item taxonomy and folksonomy
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Automatic image semantic interpretation using social action and tagging data
Multimedia Tools and Applications
Using inferred tag ratings to improve user-based collaborative filtering
Proceedings of the 27th Annual ACM Symposium on Applied Computing
LSA as ground truth for recommending "flickr-aware" representative tags
DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
User-based collaborative filtering on cross domain by tag transfer learning
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
Improving recommendation accuracy based on item-specific tag preferences
ACM Transactions on Intelligent Systems and Technology (TIST) - Special section on twitter and microblogging services, social recommender systems, and CAMRa2010: Movie recommendation in context
A novel user-based collaborative filtering method by inferring tag ratings
ACM SIGAPP Applied Computing Review
Tag recommendation by machine learning with textual and social features
Journal of Intelligent Information Systems
Personalized recommender systems integrating tags and item taxonomy
Web Intelligence and Agent Systems
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Many websites use tags as a mechanism for improving item metadata through collective user effort. Users of tagging systems often apply far more tags to an item than a system can display. These tags can range in quality from tags that capture a key facet of an item, to those that are subjective, irrelevant, or misleading. In this paper we explore tag selection algorithms that choose the tags that sites display. Based on 225,000 ratings and survey responses, we conduct offline analyses of 21 tag selection algorithms. We select the three best performing algorithms from our offline analysis, and deploy them live on the MovieLens website to 5,695 users for three months. Based on our results, we offer tagging system designers advice about tag selection algorithms.