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CPRS: A cloud-based program recommendation system for digital TV platforms
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DOI：10.1016/j.future.2010.10.002.png)
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Abstract
En 中文
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 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. (C) 2010 Elsevier B.V. All rights reserved.
Keywords:
Map-reduce
Electronic program guide (EPG)
k-nearest neighbor (kNN)
k-means
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