Proceedings of the fifth ACM conference on Recommender systems
Widget-Based simulator for testing smart space
Transactions on Edutainment III
Journal of the American Society for Information Science and Technology
Rough Set Theory Based User Aware TV Program and Settings Recommender
International Journal of Advanced Pervasive and Ubiquitous Computing
MUSST: workshop on multi-user services for social TV
Proceedings of the 11th european conference on Interactive TV and video
TV predictor: personalized program recommendations to be displayed on SmartTVs
Proceedings of the 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications
International Journal of Advanced Pervasive and Ubiquitous Computing
QA document recommendations for communities of question-answering websites
Knowledge-Based Systems
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With the increasing amount of research on smart TVs, users are interacting with them in an evermore convenient way. However, current program recommendations mainly focus on using individual profiles and require users' active participation when multiple viewers coexist. In this paper, we propose a socially aware program recommender for multiple viewers of a digital TV that rates and selects TV programs based on individual and group preferences. For this task, the proposed recommender generates recommendations for users by merging user profiles and combining their common interests. In this way, programs that all users prefer are automatically selected and ambiguous or conflicting programs are mediated based on user feedback. Through subsequent experiments with a smart TV equipped with the program recommender, we found that the performance of group recommendations improved when individual profiles and the group's common interests were used, and that users preferred different strategies when they were with other people.