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TV predictor: Personalized program recommendations to be displayed on SmartTVs

 
: Krauss, Christopher; George, Lars; Arbanowski, Stefan

:
Fulltext urn:nbn:de:0011-n-2563418 (1.1 MByte PDF)
MD5 Fingerprint: 1b4fcea1b7d30390a88766d71b2047dc
© ACM 2013 This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution.
Created on: 22.8.2013


Fan, W. ; Association for Computing Machinery -ACM-; Association for Computing Machinery -ACM-, Special Interest Group on Knowledge Discovery and Data Mining -SIGKDD-; Association for Computing Machinery -ACM-, Special Interest Group on Management of Data -SIGMOD-:
BigMine 2013, 2nd International Workshop on Big Data, Streams and Heterogeneous Source Mining: Algorithms, Systems, Programming Models and Applications. Proceedings : Chicago, August 11th, 2013
New York, NY: ACM, 2013
ISBN: 978-1-4503-2324-6
pp.63-70
International Workshop on Big Data, Streams and Heterogeneous Source Mining - Algorithms, Systems, Programming Models and Applications (BigMine) <2, 2013, Chicago/Ill.>
International Conference on Knowledge Discovery and Data Mining (KDD) <19, 2013, Chicago/Ill.>
English
Conference Paper, Electronic Publication
Fraunhofer FOKUS ()
hybrid TV; smartTV; recommendation; algorithms; contentbased filtering; collaborative filtering; offline; online

Abstract
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 T V 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 spec ialized 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.

: http://publica.fraunhofer.de/documents/N-256341.html