TY - GEN
T1 - Supporting social recommendations with activity-balanced clustering
AU - Harper, F. Maxwell
AU - Sen, Shilad
AU - Frankowski, Dan
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
KW - Activity-balanced clustering
KW - User group summarization
UR - http://www.scopus.com/inward/record.url?scp=42149171815&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=42149171815&partnerID=8YFLogxK
U2 - 10.1145/1297231.1297262
DO - 10.1145/1297231.1297262
M3 - Conference contribution
AN - SCOPUS:42149171815
SN - 9781595937308
T3 - RecSys'07: Proceedings of the 2007 ACM Conference on Recommender Systems
SP - 165
EP - 168
BT - RecSys'07
T2 - RecSys'07: 2007 1st ACM Conference on Recommender Systems
Y2 - 19 October 2007 through 20 October 2007
ER -