CPRS: A Cloud-Based Program Recommendation System for Digital TV Platforms
نویسندگان
چکیده
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 largescale 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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