ABSTRACT
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.
- Banerjee, A., Ghosh, J. Scalable Clustering with Balancing Constraints. Data Mining and Knowledge Discovery, 13(3), 2006. Google ScholarDigital Library
- Burke, R. Integrating Knowledge-Based and Collaborative Filtering Recommender Systems. Workshop on Artificial Intelligence for Electronic Commerce, 1999.Google Scholar
- Gale, D., Shapley, L. College Admissions and the Stability of Marriage. American Mathematical Monthly, 69(1), 1962.Google Scholar
- Harper, F., Frankowski, D., Drenner, S., Ren, Y., Kiesler, S., Terveen, L., Kraut, R., Riedl, J. Talk Amongst Yourselves: Inviting Users To Participate In Online Conversations. IUI, 2007. Google ScholarDigital Library
- He, J., Tan, A., Tan, C., Sung, S. On Quantitative Evaluation of Clustering Systems. In Information Retrieval and Clustering, Kluwer Academic Publishers, 2002.Google Scholar
- Jain, A., Murty, M., Flynn, P. Data Clustering: A Review. ACM Computing Surveys, 31(3), 1999. Google ScholarDigital Library
- Popescul, A., Ungar, L. Automatic Labeling of Document Clusters, Unpublished Manuscript, Available at http://citeseer.nj.nec.com/popescul00automatic.html, 2000.Google Scholar
- Preece, J. Online Communities: Designing Usability, Supporting Sociability. John Wiley & Sons, 2000. Google ScholarDigital Library
- Sarwar, B., Karypis, G., Konstan, J., Riedl, J. Item-Based Collaborative Filtering Recommendation Algorithms. WWW, 2001. Google ScholarDigital Library
- Sen, S., Lam, S., Cosley, D., Rashid, A., Frankowski, D., Osterhouse, J., Harper, F., Riedl, J. tagging, community, vocabulary, evolution. CSCW, 2006. Google ScholarDigital Library
Index Terms
- Supporting social recommendations with activity-balanced clustering
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