Amazon.com Recommendations: Item-to-Item Collaborative Filtering
IEEE Internet Computing
Content-boosted collaborative filtering for improved recommendations
Eighteenth national conference on Artificial intelligence
Probabilistic Memory-Based Collaborative Filtering
IEEE Transactions on Knowledge and Data Engineering
IEEE Transactions on Knowledge and Data Engineering
Java Data Mining: Strategy, Standard, and Practice: A Practical Guide for architecture, design, and implementation
A Survey of Explanations in Recommender Systems
ICDEW '07 Proceedings of the 2007 IEEE 23rd International Conference on Data Engineering Workshop
IIS '09 Proceedings of the 2009 International Conference on Industrial and Information Systems
Towards an Introduction to Collaborative Filtering
CSE '09 Proceedings of the 2009 International Conference on Computational Science and Engineering - Volume 04
Information Sciences: an International Journal
A Hybrid Collaborative Filtering Algorithm Based on User-Item
ICCIS '10 Proceedings of the 2010 International Conference on Computational and Information Sciences
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Socially aware tv program recommender for multiple viewers
IEEE Transactions on Consumer Electronics
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Switching through the variety of available TV channels to find the most acceptable program at the current time can be very time-consuming. Especially at the prime time when there are lots of different channels offering quality content it is hard to find the best fitting channel. This paper introduces the TV Predictor, a new application that allows for obtaining personalized program recommendations without leaving the lean back position in front of the TV. Technically the usage of common Standards and Specifications, such as HbbTV, OIPF and W3C, leverage the convergence of broadband and broadcast media. Hints and details can overlay the broadcasting signal and so the user gets predictions in appropriate situations, for instance the most suitable movies playing tonight. Additionally the TV Predictor Autopilot enables the TV set to automatically change the currently viewed channel. A Second Screen Application mirrors the TV screen or displays additional content on tablet PCs and Smartphones. Based on the customers viewing behavior and explicit given ratings the server side application predicts what the viewer is going to favor. Different data mining approaches are combined in order to calculate the users preferences: Content Based Filtering algorithms for similar items, Collaborative Filtering algorithms for rating predictions, Clustering for increasing the performance, Association Rules for analyzing item relations and Support Vector Machines for the identification of behavior patterns. A ten fold cross validation shows an accuracy in prediction of about 80%. TV specialized User Interfaces, user generated feedback data and calculated algorithm results, such as Association Rules, are analyzed to underline the characteristics of such a TV based application.