X-means: Extending K-means with Efficient Estimation of the Number of Clusters
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
A Fast Approximated k-Median Algorithm
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
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
PolyLens: a recommender system for groups of users
ECSCW'01 Proceedings of the seventh conference on European Conference on Computer Supported Cooperative Work
Real-time embedded face recognition for smart home
IEEE Transactions on Consumer Electronics
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The television as a multi-user device presents some specificities with respect to personalisation. Recommendations should be provided both per-viewers as well as for a group. Recognising the inadequacy of traditional user modelling techniques with the constraint of television's lazy watching usage patterns, this paper presents a new recommendation mechanism based on anonymous user preferences and dynamic filtering of recommendations. Results from an initial user study indicate this mechanism was able to provide content recommendations to individual users within a multi-user environment with a high level of user satisfaction and without the need for user authentication or individual preference profile creation.