Digital TV Program Recommendation System Based on Latent Factor Model

Abstract:

In order to solve the problem of information overload, which is a consequence of the extreme abundance of digital television (TV) program resource, this paper proposed a digital TV program recommendation system based on latent factor model (LFM). This system constructed a digital TV program recommendation system structure including information inputting unit, system analysis unit and information recommendation sending unit. The proposed program type analysis method searches for the relation between audience interest and the programs which audiences watch. To classify the audience crowd, there are two types of analysis method which are program type threshold analysis and program type cluster analysis that based on artificial classification of experience value and automatic clustering with arbitrary number of clusters respectively. The feasibility of the algorithm has been proved by simulated analysis.

• Periodical: Applied Mechanics and Materials (Volumes 513-517)
• Pages: 1692-1695
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.513-517.1692
• Online since: February 2014
• Authors: Fu Lian Yin*, Jian Ping Chai, Nan Li, Ya Gao
• Keywords: Digital Television, Program Recommendation System, Program Type Analysis, Program Type Cluster Analysis, Program Type Threshold Analysis

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