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.
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Chin-Feng, L., Jui-Hung, C., Chia-Cheng, H., Yueh-Min, H., Han-Chieh, C. (2010). CPRS: A Cloud-Based Program Recommendation System for Digital TV Platforms. In: Bellavista, P., Chang, RS., Chao, HC., Lin, SF., Sloot, P.M.A. (eds) Advances in Grid and Pervasive Computing. GPC 2010. Lecture Notes in Computer Science, vol 6104. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13067-0_36
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DOI: https://doi.org/10.1007/978-3-642-13067-0_36
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