CPRS: a cloud-based program recommendation system for digital TV platforms

Authors:
Lai Chin-Feng;Chang Jui-Hung;Hu Chia-Cheng;Huang Yueh-Min;Chao Han-Chieh
Affiliations:
Department of Engineering Science, National Chung Kung University, Tainan, Taiwan;Department of Engineering Science, National Chung Kung University, Tainan, Taiwan;Department of Information Management, Naval Academy, Kaohsiung, Taiwan;Department of Engineering Science, National Chung Kung University, Tainan, Taiwan;College of Electrical Engineering and Computer Science, National Ilan University, Ilan, Taiwan
Venue:
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
Year:
2010

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Abstract

Traditional electronic program guides (EPGs) cannot be used to find popular TV programs A personalized digital video broadcasting – terrestrial (DVB-T) digital TV program recommendation system is ideal for providing TV program suggestions based on statistics results obtained from analyzing large-scale data The frequency and duration of the programs that users have watched are collected and weighted by data mining techniques A large dataset produces results that best represent a viewer's preferences of TV programs in a specific area To process such a massive amount viewer preference data, the bottleneck of scalability and computing power must be removed In this paper, an architecture for a TV program recommendation system based on cloud computing and a map-reduce framework, the map-reduce version of k-means and the k-nearest neighbor (kNN) algorithm, is introduced and applied The proposed architecture provides a scalable and powerful backend to support the demand of large-scale data processing for a program recommendation system.