Communications of the ACM
Combining collaborative filtering with personal agents for better recommendations
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Content-based book recommending using learning for text categorization
DL '00 Proceedings of the fifth ACM conference on Digital libraries
TV anytime as an application scenario for MPEG-7
MULTIMEDIA '00 Proceedings of the 2000 ACM workshops on Multimedia
Evaluation of Item-Based Top-N Recommendation Algorithms
Proceedings of the tenth international conference on Information and knowledge management
Methods and metrics for cold-start recommendations
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Evaluation of subjective video quality of mobile devices
Proceedings of the 13th annual ACM international conference on Multimedia
Can small be beautiful?: assessing image resolution requirements for mobile TV
Proceedings of the 13th annual ACM international conference on Multimedia
TV Program Recommendation for Multiple Viewers Based on user Profile Merging
User Modeling and User-Adapted Interaction
Fuzzy Recommendation towards QoS-Aware Pervasive Learning
AINA '07 Proceedings of the 21st International Conference on Advanced Networking and Applications
I tube, you tube, everybody tubes: analyzing the world's largest user generated content video system
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Lessons from the Netflix prize challenge
ACM SIGKDD Explorations Newsletter - Special issue on visual analytics
Proceedings of the 1st international conference on Designing interactive user experiences for TV and video
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
A living lab research approach for mobile TV
Proceedings of the seventh european conference on European interactive television conference
Mobile Networks and Applications
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
TV3P: an adaptive assistant for personalized TV
IEEE Transactions on Consumer Electronics
A personalized TV guide system compliant with MHP
IEEE Transactions on Consumer Electronics
Users' (Dis)satisfaction with the personalTV application: Combining objective and subjective data
Computers in Entertainment (CIE) - Theoretical and Practical Computer Applications in Entertainment
Recent advances and future directions in multimedia and mobile computing
Multimedia Tools and Applications
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The efficiency of personal video suggestions generated by recommender systems is highly dependent on the quality of the obtained user feedback. This feedback has to reflect the personal interest in the content of the viewed video, to obtain accurate results. However, user feedback might undesirably be influenced by additional aspects such as the loading speed or the quality of the video. To date, this issue has received very little research attention. Therefore, this study investigates the direct influence of audio-visual quality parameters on explicit user feedback for the first time to our knowledge via a mobile, Living Lab experiment. This paper proposes a feedback model which takes the Quality of Service (QoS) parameters of the mobile network into account. This model can be used as an additional feedback filter for video recommendation systems that could help to eliminate the influences of QoS on explicit user feedback.