Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
A Framework for Collaborative, Content-Based and Demographic Filtering
Artificial Intelligence Review - Special issue on data mining on the Internet
Introduction to Modern Information Retrieval
Introduction to Modern Information Retrieval
Dependency networks for inference, collaborative filtering, and data visualization
The Journal of Machine Learning Research
Group Modeling: Selecting a Sequence of Television Items to Suit a Group of Viewers
User Modeling and User-Adapted Interaction
Evaluating collaborative filtering recommender systems
ACM Transactions on Information Systems (TOIS)
TiVo: making show recommendations using a distributed collaborative filtering architecture
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 10th international conference on Intelligent user interfaces
Trust-based agent community for collaborative recommendation
AAMAS '06 Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems
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
Youtube traffic characterization: a view from the edge
Proceedings of the 7th ACM SIGCOMM conference on Internet measurement
Personalized and mobile digital TV applications
Multimedia Tools and Applications
Structure and Network in the YouTube Core
HICSS '08 Proceedings of the Proceedings of the 41st Annual Hawaii International Conference on System Sciences
Social software: fun and games, or business tools?
Journal of Information Science
Identifying user behavior in online social networks
Proceedings of the 1st Workshop on Social Network Systems
Who predicts better?: results from an online study comparing humans and an online recommender system
Proceedings of the 2008 ACM conference on Recommender systems
Broadcast yourself on YouTube: really?
HCC '08 Proceedings of the 3rd ACM international workshop on Human-centered computing
Context aware recommendations for user-generated content on a social network site
Proceedings of the seventh european conference on European interactive television conference
Recommenders' influence on buyers' decision process
Proceedings of the third ACM conference on Recommender systems
Hybrid web recommender systems
The adaptive web
How useful are your comments?: analyzing and predicting youtube comments and comment ratings
Proceedings of the 19th international conference on World wide web
The YouTube video recommendation system
Proceedings of the fourth ACM conference on Recommender systems
A user-centric evaluation framework for recommender systems
Proceedings of the fifth ACM conference on Recommender systems
UAI'01 Proceedings of the Seventeenth conference on Uncertainty in artificial intelligence
Empirical analysis of predictive algorithms for collaborative filtering
UAI'98 Proceedings of the Fourteenth conference on Uncertainty in artificial intelligence
Alleviating the sparsity problem of collaborative filtering using trust inferences
iTrust'05 Proceedings of the Third international conference on Trust Management
Investigating the influence of QoS on personal evaluation behaviour in a mobile context
Multimedia Tools and Applications
What's on TV tonight? An efficient and effective personalized recommender system of TV programs
IEEE Transactions on Consumer Electronics
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The overabundance of content on online video platforms has made intelligent recommender systems that assist users in finding content matching their personal preferences indispensable. This article reports on a study in which “PersonalTV,” an online video recommendation application that has been developed for research purposes, was evaluated by a panel of test users for the first time. In view of this, objective implicit and subjective explicit user feedback were triangulated. The “PersonalTV” application enables its users to explore and watch videos from the YouTube library. It builds up a personal viewing profile in order to give personalized content suggestions. We investigated the relation between the recommended content and the consumption percentage (RQ 1), between the recommended content and the reported satisfaction (RQ 2), and explored whether these objective and subjective measures converge (RQ 3). Additional user feedback that may help to improve the application was collected.