Using collaborative filtering to weave an information tapestry
Communications of the ACM - Special issue on information filtering
GroupLens: an open architecture for collaborative filtering of netnews
CSCW '94 Proceedings of the 1994 ACM conference on Computer supported cooperative work
MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Item-based collaborative filtering recommendation algorithms
Proceedings of the 10th international conference on World Wide Web
Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
The Journal of Machine Learning Research
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Latent semantic models for collaborative filtering
ACM Transactions on Information Systems (TOIS)
The author-topic model for authors and documents
UAI '04 Proceedings of the 20th conference on Uncertainty in artificial intelligence
IEEE Transactions on Knowledge and Data Engineering
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Group modeling in a public space: methods, techniques, experiences
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
A survey of collaborative filtering techniques
Advances in Artificial Intelligence
The adaptive web
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Event-based social networks: linking the online and offline social worlds
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Exploring social influence for recommendation: a generative model approach
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
Combining latent factor model with location features for event-based group recommendation
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
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Group activities are essential ingredients of people's social life. The rapid growth of online social networking services has greatly boosted group activities by providing convenient platform for users to organize and participate in such activities. Therefore, recommender systems, as a critical component in social networking services, now face new challenges in supporting group activities. In this paper, we study the group recommendation problem, i.e., making recommendations to a group of people in social networking services. We analyze the decision making process in a group to propose a personal impact topic (PIT) model for group recommendations. The PIT model effectively identifies the group preference profile for a given group by considering the personal preferences and personal impacts of group members. Moreover, we further enhance the discovery of personal impact with social network information to obtain an extended personal impact topic (E-PIT) model. We have conducted comprehensive data analysis and evaluations on three real datasets. The results show that our proposed group recommendation techniques outperform baseline approaches.