Program Popularity Prediction Model of Internet TV Based on Viewing Behavior

ZHU Chengang; CHENG Guang

doi:10.11999/JEIT161310

Volume 39 Issue 10

Oct. 2017

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ZHU Chengang, CHENG Guang. Program Popularity Prediction Model of Internet TV Based on Viewing Behavior[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2504-2512. doi: 10.11999/JEIT161310

Citation:

ZHU Chengang, CHENG Guang. Program Popularity Prediction Model of Internet TV Based on Viewing Behavior[J]. Journal of Electronics & Information Technology, 2017, 39(10): 2504-2512. doi: 10.11999/JEIT161310

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Program Popularity Prediction Model of Internet TV Based on Viewing Behavior

doi: 10.11999/JEIT161310

ZHU Chengang¹,
CHENG Guang¹

Funds:

The National 863 Program of China (2015AA 015603), The Prospective Research Program on Future Networks of Jiangsu Province (BY2013095-5-03), The Six Industries Talent Peaks Plan of Jiangsu Province (2011-DZ024)

Received Date: 2016-12-08
Rev Recd Date: 2017-06-15
Publish Date: 2017-10-19

Abstract

Abstract

Predicting program popularity is a key issue for design and optimization of Internet TV system. Existing prediction methods usually need large quantity of samples and long training time, while the prediction accuracy is poor for the burst hot programs. This paper introduces an Internet TV Program Popularity Prediction model based on viewing Behavioral Dynamics features (BD3P). 6 billion view behavior records from 2.8 million subscribers of a certain Internet TV platform are measured, and the evolution process of program popularity is divided into 4 types based on behavioral dynamics features, which is endogenous, internal subcritical, exogenous and exogenous subcritical. The prediction models of Internet TV program popularity are constructed for each type using Least Squares Support Vector Machines (LSSVM) with double population Particle Swarm Optimization (PSO), and these models are applied to the actual data test. The experimental results show that, compared to the existing prediction model, the prediction accuracy can be increased by more than 17%, and the forecast period can be effectively shortened.

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References(15)

References

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