MusicFX: an arbiter of group preferences for computer supported collaborative workouts
CSCW '98 Proceedings of the 1998 ACM conference on Computer supported cooperative work
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
Preference aggregation in group recommender systems for committee decision-making
Proceedings of the third ACM conference on Recommender systems
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
Group recommendations with rank aggregation and collaborative filtering
Proceedings of the fourth ACM conference on Recommender systems
New approaches to mood-based hybrid collaborative filtering
Proceedings of the Workshop on Context-Aware Movie Recommendation
Recommender systems with social regularization
Proceedings of the fourth ACM international conference on Web search and data mining
Matrix factorization techniques for context aware recommendation
Proceedings of the fifth ACM conference on Recommender systems
Group recommendation in context
Proceedings of the 2nd Challenge on Context-Aware Movie Recommendation
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Nowadays most recommender systems are made for individuals. However, there is a need to offer recommendations to a group rather than an individual in many scenarios, such as interactive TV watched in a family, friends traveling together. Taking consideration of group information as an additional context has also been a challenge in context-aware recommender systems. In this work, we propose some SVD-based group recommendation methods through aggregating ratings of group members with different group decision strategies, including weighted, least misery, and hybrid ones. These methods are divided into two categories: SVD-based aggregation profiles and aggregation predictions methods. The former ones employ "group aggregation first, SVD-based prediction later", while the latter ones are opposite. Finally we conduct some experiments on the Moviepilot dataset released for the Challenge on Context-Aware Movie Recommendation (CAMRa2011) to evaluate the effectiveness of different SVD-based group recommendation approaches, and analyze the results.