Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
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
Profile Generation from TV Watching Behavior Using Sentiment Analysis
WI-IATW '07 Proceedings of the 2007 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Workshops
Group recommendation: semantics and efficiency
Proceedings of the VLDB Endowment
A Survey of Accuracy Evaluation Metrics of Recommendation Tasks
The Journal of Machine Learning Research
Performance of recommender algorithms on top-n recommendation tasks
Proceedings of the fourth ACM conference on Recommender systems
Group-based recipe recommendations: analysis of data aggregation strategies
Proceedings of the fourth ACM conference on Recommender systems
Development of a TV reception navigation system personalized with viewing habits
IEEE Transactions on Consumer Electronics
TV program recommendation for groups based on muldimensional TV-anytime classifications
IEEE Transactions on Consumer Electronics
Socially aware tv program recommender for multiple viewers
IEEE Transactions on Consumer Electronics
A group recommendation approach for service selection
Proceedings of the Fourth Asia-Pacific Symposium on Internetware
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This paper proposes an algorithm to estimate appropriate or novel content for groups of people who know each other such as friends, couples, and families. To achieve high recommendation accuracy, we focus on "Groupality", the entity or entities that characterize groups such as the tendency of content selection and the relationships among group members. Our algorithm calculates recommendation scores using a feature space that consists of the behavioral tendency of a group and the power balance among group members based on individual preference and the behavioral history of group. After gathering the behavioral history of subject groups when watching TV, we verify that our proposed algorithm can recommend appropriate content, and find novel content. Evaluations show that our proposal achieves higher performance than existing methods.