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

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 of 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 kk-means and the kk-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.

Research highlights
► The CPRS was implemented to improve existing television channel recommendation systems.
► Apply Traditional EPG to discover the user’s behavior pattern in the TV program, then build a program recommendation system.
► CPRS: Cloud-Based Program Recommendation System. EPG: Electronic Program Guides.
► The system was implemented by a cloud computing framework and used the map-reduce programming model.
► The map-reduce versions of kk-means and kNN, the two core algorithms in the proposed system.