ACM Computing Surveys (CSUR)
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Online Communities: Designing Usability and Supporting Socialbilty
Online Communities: Designing Usability and Supporting Socialbilty
Scalable Clustering Algorithms with Balancing Constraints
Data Mining and Knowledge Discovery
tagging, communities, vocabulary, evolution
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
Talk amongst yourselves: inviting users to participate in online conversations
Proceedings of the 12th international conference on Intelligent user interfaces
Web-Based Recommender Systems and User Needs --the Comprehensive View
Proceedings of the 2008 conference on New Trends in Multimedia and Network Information Systems
Improving matching process in social network using implicit and explicit user information
APWeb'11 Proceedings of the 13th Asia-Pacific web conference on Web technologies and applications
Putting humans in the loop: Social computing for Water Resources Management
Environmental Modelling & Software
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In support of social interaction and information sharing, online communities commonly provide interfaces for users to form or interact with groups. For example, a user of the social music recommendation site last.fm might join the "First Wave Punk" group to discuss his or her favorite band (The Clash) and listen to playlists generated by fellow fans. Clustering techniques provide the potential to automatically discover groups of users who appear to share interests. We explore this idea by describing algorithms for clustering users of an online community and automatically describing the resulting user groups. We designed these techniques for use in an online recommendation system with no pre-existing group functionality, which led us to develop an "activity-balanced clustering" algorithm that considers both user activity and user interests in forming clusters.