Program Popularity Prediction Model of Internet TV Based on Viewing Behavior
ZHU Chengang CHENG Guang
(School of Computer Science and Engineering, Southeast University, Nanjing 211189, China)
(Key Laboratory of Computer Network and Information Integration, Ministry of Education (Southeast University), Nanjing 211189, China)
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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