MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
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
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
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
A profiling engine for converged service delivery platforms
Bell Labs Technical Journal - Applications and their Enablers in a Converged Communications World
Informative household recommendation with feature-based matrix factorization
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games
Proceedings of the 22nd international conference on World Wide Web companion
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Most of the existing personalization systems such as content recommenders or targeted ads focus on individual users and ignore the social situation in which the services are consumed. However, many human activities are social and involve several individuals whose tastes and expectations must be taken into account by the system. When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate items to a group of users based on their individual profiles. We consider an approach intended to determine the factors that influence the choice of an aggregation strategy. We present evaluations made on a large-scale dataset of TV viewings, where real group interests are compared to the predictions obtained by combining individual user profiles according to different strategies.