MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
Let's browse: a collaborative Web browsing agent
IUI '99 Proceedings of the 4th international conference on Intelligent user interfaces
Flytrap: intelligent group music recommendation
Proceedings of the 7th international conference on Intelligent user interfaces
UniCast, OutCast & GroupCast: Three Steps Toward Ubiquitous, Peripheral Displays
UbiComp '01 Proceedings of the 3rd international conference on Ubiquitous Computing
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
More than the sum of its members: challenges for group recommender systems
Proceedings of the working conference on Advanced visual interfaces
Exploiting Query Repetition and Regularity in an Adaptive Community-Based Web Search Engine
User Modeling and User-Adapted Interaction
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
Personalizing access to learning networks
ACM Transactions on Internet Technology (TOIT)
Group modeling in a public space: methods, techniques, experiences
AIC'05 Proceedings of the 5th WSEAS International Conference on Applied Informatics and Communications
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
Analysis of strategies for building group profiles
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
Choosing which message to publish on social networks: a contextual bandit approach
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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In this paper we propose a group recommendation model that infers the group preferences based on the actions of its members. Such recommendations can be useful when individuals are working together in areas that require relevant up-to-date news information to support decisions. To test our model, we used Twitter, a microblogging service, as a platform to recommend links to news articles. To evaluate our model, we compared the group satisfaction with different strategies of group recommendation. Results show that our model obtained an average group rating of 3.58 out of 5 over the recommendations given to the group. This represents an improvement of approximately 25% over the best performing strategy we tested. We also analyzed the impact of different actions on Twitter and of a time decay parameter on group satisfaction