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
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
The FindMe Approach to Assisted Browsing
IEEE Expert: Intelligent Systems and Their Applications
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
Adaptive radio: achieving consensus using negative preferences
GROUP '05 Proceedings of the 2005 international ACM SIGGROUP conference on Supporting group work
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
The needs of the many: a case-based group recommender system
ECCBR'06 Proceedings of the 8th European conference on Advances in Case-Based Reasoning
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
Space efficiency in group recommendation
The VLDB Journal — The International Journal on Very Large Data Bases
Recommending Flickr groups with social topic model
Information Retrieval
Generating recommendations for consensus negotiation in group personalization services
Personal and Ubiquitous Computing
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There are increasingly many recommendation scenarios where recommendations must be made to satisfy groups of people rather than individuals. This represents a significant challenge for current recommender systems because they must now cope with the potentially conflicting preferences of multiple users when selecting items for recommendation. In this paper we focus on how individual user models can be aggregated to produce a group model for the purpose of biasing recommendations in a critiquing-based, case-based recommender. We describe and evaluate 3 different aggregation policies and highlight the benefits of group recommendation using live-user preference data.