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
Proceedings of the 3rd ACM SIGCHI symposium on Engineering interactive computing systems
Extending sound sample descriptions through the extraction of community knowledge
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Towards effective group recommendations for microblogging users
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
Tailoring recommendations to groups of users: a graph walk-based approach
Proceedings of the 2013 international conference on Intelligent user interfaces
Users' satisfaction in recommendation systems for groups: an approach based on noncooperative games
Proceedings of the 22nd international conference on World Wide Web companion
Towards effective course-based recommendations for public tenders
International Journal of Knowledge and Web Intelligence
| Hi-index | 0.00 |
Today most of existing personalization systems (e.g content recommenders, or targeted ad) 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 service providers When a group profile is not available, different profile aggregation strategies can be applied to recommend adequate content and services to a group of users based on their individual profiles In this paper, we consider an approach intended to determine the factors that influence the choice of an aggregation strategy We present a preliminary evaluation made on a real large-scale dataset of TV viewings, showing how group interests can be predicted by combining individual user profiles through an appropriate strategy The conducted experiments compare the group profiles obtained by aggregating individual user profiles according to various strategies to the “reference” group profile obtained by directly analyzing group consumptions.