TY - GEN
T1 - CPRS
T2 - 5th International Conference on Advances in Grid and Pervasive Computing, GPC 2010
AU - Chin-Feng, Lai
AU - Jui-Hung, Chang
AU - Chia-Cheng, Hu
AU - Yueh-Min, Huang
AU - Han-Chieh, Chao
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=77953793637&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77953793637&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-13067-0_36
DO - 10.1007/978-3-642-13067-0_36
M3 - Conference contribution
AN - SCOPUS:77953793637
SN - 3642130666
SN - 9783642130663
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 331
EP - 340
BT - Advances in Grid and Pervasive Computing - 5th International Conference, GPC 2010, Proceedings
Y2 - 10 May 2010 through 13 May 2010
ER -