Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior

QIAO Sibo; PANG Shanchen; WANG Min; ZHAI Xue; DAI Feng

doi:10.1049/cje.2021.06.010

Volume 30 Issue 5

Sep. 2021

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Article Navigation > Chinese Journal of Electronics > 2021 > 30(5): 876-884

QIAO Sibo, PANG Shanchen, WANG Min, ZHAI Xue, DAI Feng. Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior[J]. Chinese Journal of Electronics, 2021, 30(5): 876-884. doi: 10.1049/cje.2021.06.010

Citation:

QIAO Sibo, PANG Shanchen, WANG Min, ZHAI Xue, DAI Feng. Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior[J]. Chinese Journal of Electronics, 2021, 30(5): 876-884. doi: 10.1049/cje.2021.06.010

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Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior

doi: 10.1049/cje.2021.06.010

1. College of Computer Science and Technology, China University of Petroleum, Qingdao 266580, China;
2. College of Control Science and Engineering, China University of Petroleum, Qingdao 266580, China;
3. College of Information Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China

Funds:

This work is supported by the National Natural Science Foundation of China (No.61873281), Major Science and Technology Innovation Project of Shandong Province (No.2019TSLH0214), and Tai Shan Industry Leading Talent Project (No.tscy20180416).

Received Date: 2020-06-11
Available Online: 2021-09-02

Abstract

Abstract

Popularity prediction of online video is widely used in many different scenarios. It can not only help video service providers to schedule video web sites, but also bring considerable profits on investment for both providers and advertisers if popularity of online video is predicted accurately. However, online video popularity prediction still cannot have a satisfactory result, due to the complexity of many crucial factors especially of video distribution network. In this article, we extract seven factors from huge amounts of data about user behavior, establishing a new multiple linear regression model to initially predict online video popularity. After that, a multichannel video popularity dynamic scheduling model is proposed to schedule videos on which channel and what time to be broadcast, according to its popularity predicted by multiple linear regression model, ensuring that maximum the sum value of online video popularity of each channel. Experimental results on dataset obtained from Sohu Video, a video service provider in China, and real-world video flow in Sohu Video demonstrate that the proposed model is robust and has promising performance in predicting online video popularity, which is helpful for video service providers to schedule videos on web sites effectively in the future.
- Online video popularity prediction,
- Channel optimization,
- User behavior,
- Multiple linear regression

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References(35)

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