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Program Popularity Prediction Approach for Internet TV Based on Trend Detecting

  • Chengang Zhu
  • Guang ChengEmail author
  • Kun Wang
Conference paper
  • 603 Downloads
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 221)

Abstract

Predicting program popularity precisely and timely is of great value for content providers, advertisers, as well as Internet TV operators. Existing prediction methods usually need large quantity of samples and long training time, while the prediction accuracy is poor for the programs that experience a high peak or sharp decrease in popularity. This paper presents our improved prediction approach based on trend detecting. First, we apply a dynamic time warping (DTW) distance based k-medoids algorithm to group programs popularity evolution into 4 trends. Then, 4 trend-specific prediction models are built separately using random forests (RF) regression. According to the features extracted from electronic program guide (EPG) and early view records, newly published programs are classified into the 4 trends by a gradient boosting decision tree. Finally, combining forecasting values from the trend-specific models and classification probability, our proposed approach achieves better prediction results. The experimental results show that, compared to the existing prediction models, the prediction accuracy can increase more than 20%, and the forecasting period can be effectively shortened.

Keywords

Internet TV Popularity prediction Dynamic time warping Random forests regression Gradient boosting decision tree 

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Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringSoutheast UniversityDhakaBangladesh
  2. 2.Key Laboratory of Computer Network and Information Integration, Ministry of EducationSoutheast UniversityDhakaBangladesh
  3. 3.School of Internet of ThingsNanjing University of Posts and TelecommunicationsNanjingChina

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