Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Effective explanations of recommendations: user-centered design
Proceedings of the 2007 ACM conference on Recommender systems
APCHI '08 Proceedings of the 8th Asia-Pacific conference on Computer-Human Interaction
Recommending scientific articles using citeulike
Proceedings of the 2008 ACM conference on Recommender systems
Personalized recommendation of social software items based on social relations
Proceedings of the third ACM conference on Recommender systems
TagiCoFi: tag informed collaborative filtering
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
Social networks and interest similarity: the case of CiteULike
Proceedings of the 21st ACM conference on Hypertext and hypermedia
Leveraging the linkedin social network data for extracting content-based user profiles
Proceedings of the fifth ACM conference on Recommender systems
Proceedings of the 4th ACM RecSys workshop on Recommender systems and the social web
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This paper aims to combine information about users' self-defined social connections with traditional collaborative filtering (CF) to improve recommendation quality. Specifically, in the following, the users' social connections in consideration were groups. Unlike other studies which utilized groups inferred by data mining technologies, we used the information about the groups in which each user explicitly participated. The group activities are centered on common interests. People join a group to share and acquire information about a topic as a form of community of interest or practice. The information of this group activity may be a good source of information for the members. We tested whether adding the information from the users' own groups or group members to the traditional CF-based recommendations can improve the recommendation quality or not. The information about groups was combined with CF using a mixed hybridization strategy. We evaluated our approach in two ways, using the Citeulike data set and a real user study.