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

Turn off MathJax

Article Contents

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

QIAO Sibo, PANG Shanchen, WANG Min, et al., “Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 876-884, 2021, doi: 10.1049/cje.2021.06.010

Citation:

QIAO Sibo, PANG Shanchen, WANG Min, et al., “Online Video Popularity Regression Prediction Model with Multichannel Dynamic Scheduling Based on User Behavior,” Chinese Journal of Electronics, vol. 30, no. 5, pp. 876-884, 2021, doi: 10.1049/cje.2021.06.010

Citation:

PDF( 1482 KB)

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

FullText(HTML)

References(35)

References

V. Almeida, A. Bestavros, M. Crovella, et al., "Characterizing reference locality in the WWW", Fourth International Conference on Parallel and Distributed Information Systems, pp.92-103, 1996.

M. Chesire, A. Wolman, G. Voelker, et al., "Measurement and analysis of a streaming-media workload", The 3rd Conference on USENIX Symposium on Internet Technologies and Systems, Vol.3, pp.1-12, 2001.

F. Figueiredo, F. Benevenuto and J. Almeida, "The tube over time:Characterizing popularity growth of YouTube videos", Forth International Conference on Web Search and Web Data Mining, pp.745-754, 2011.

S. Ouyang, C. Li and X. Li, "Analyzing the dynamics of online video popularity", Journal of China Universities of Posts and Telecommunications, Vol.24, No.3, pp.58-69, 2017.

C. Castillo, M. El-Haddad, J. Pfeffer, et al., "Characterizing the life cycle of online news stories using social media reactions", ACM Conference on Computer Supported Cooperative Work and Social Computing, pp.211-223, 2014.

A. Tatar, M. De Amorim, S. Fdida, et al., "A survey on predicting the popularity of web content", Journal of Internet Services and Applications, Vol.5, No.1, Page 8, 2014.

M. Tsagkias, W. Weerkamp, M. De Rijke, et al., "News comments:Exploring, modeling, and online prediction", European Conference on Information Retrieval Research, pp.191-203, 2010.

L. Jiang, Y. Miao, Y. Yang, et al., "Viral video style:A closer look at viral videos on YouTube", International Conference on Multimedia Retrieval, pp.193-200, 2014.

J. Wu, Y. Zhou, D. Chiu, et al., "Modeling dynamics of online video popularity", IEEE Transactions on Multimedia, Vol.18, No.9, pp.1882-1895, 2016.

T. Rodrigues, F. Benevenuto, V. Almeida, et al., "Equal but different:A contextual analysis of duplicated videos on YouTube", Journal of the Brazilian Computer Society, Vol.16, No.3, pp.201-214, 2010.

H. Pinto, J. Almeida and M. Gonçalves, "Using early view patterns to predict the popularity of youtube videos", ACM International Conference on Web Search and Data Mining, pp.365-374, 2013.

J. Xu, M. Van, J. Liu, et al., "Forecasting popularity of videos using social media", IEEE Journal on Selected Topics in Signal Processing, Vol.9, No.2, pp.330-343, 2015.

A. Khosla, A. Das and R. Hamid, "What makes an image popular?", International Conference on World Wide Web, pp.867-876, 2014.

T. Trzciński, P. Andruszkiewicz, T. Bocheński, et al., "Recurrent neural networks for online video popularity prediction", International Symposium on Methodologies for Intelligent Systems, pp.146-153, 2017.

T. Trzcinski and P. Rokita, "Predicting popularity of online videos using support vector regression", IEEE Transactions on Multimedia, Vol.19, No.11, pp.2561-2570, 2017.

W. Stokowiec, T. Trzciński, K. Wolk, et al., "Shallow reading with deep learning:Predicting popularity of online content using only its title", International Symposium on Methodologies for Intelligent Systems, pp.136-145, 2017.

A. Deza and D. Arturo, "Understanding image virality", IEEE Conference on Computer Vision and Pattern Recognition, pp.1818-1826, 2015.

A. Bielski and T. Trzcinski, "Understanding multimodal popularity prediction of social media videos with selfattention", IEEE Access, Vol.6, pp.74277-74287, 2018.

L. Hong, O. Dan and B. Davison, "Predicting popular messages in Twitter", International Conference Companion on World Wide Web, pp.57-58,2011.

G. Chen, Q. Kong and W. Mao, "An attention-based neural popularity prediction model for social media events", International Conference on Intelligence and Security Informatics:Security and Big Data, pp.161-163, 2017.

Y. Bae and H. Lee, "Sentiment analysis of twitter audiences:Measuring the positive or negative influence of popular twitterers", Journal of the American Society for Information Science and Technology, Vol.63, No.12, pp.2521-2535, 2012.

Z. Ma, A. Sun and G. Cong, "On predicting the popularity of newly emerging hashtags in Twitter", Journal of the American Society for Information Science and Technology, Vol.64, No.7, pp.1399-1410, 2013.

S. Cappallo, T. Mensink and C. Snoek, "Latent factors of visual popularity prediction", ACM International Conference on Multimedia Retrieval, pp.195-202, 2015.

F. Gelli, T. Uricchio, M. Bertini, et al., "Image popularity prediction in social media using sentiment and context features", ACM International Conference on Multimedia, pp.907-910, 2015.

H. Li, X. Ma, F. Wang, et al., "On popularity prediction of videos shared in online social networks", ACM International Conference on Information and Knowledge Management, pp.169-178, 2013.

P. Jing, Y. Su, L. Nie, et al., "Low-Rank multi-view embedding learning for micro-video popularity prediction", IEEE Transactions on Knowledge and data engineering, Vol.30, No.8, pp.1519-1532, 2018.

M. Vasconcelos, J. Almeida and M. Gonçalves, "Predicting the popularity of micro-reviews:A Foursquare case study", Information Sciences, Vol.325, pp.355-374, 2015.

B. Wu and H. Shen, "Analyzing and predicting news popularity on Twitter", International Journal of Information Management, Vol.35, No.6, pp.702-711, 2015.

S. Roy, T. Mei, W. Zen, et al., "Towards cross-domain learning for social video popularity prediction", IEEE Transactions on Multimedia, Vol.15, No.6, pp.1255-1267, 2013.

W. Ding, Y. Shang, L. Guo, et al., "Video popularity prediction by sentiment propagation via implicit network", ACM International Conference on Information and Knowledge Management, pp.1621-1630, 2015.

M. Meghawat, S. Yadav, D. Mahata, et al., "A multimodal approach to predict social media popularity", IEEE Conference on Multimedia Information Processing and Retrival, pp.190-195, 2018.

W. Zhang, W. Wang, J. Wang, et al., "User-guided hierarchical attention network for multi-modal social image popularity prediction", IEEE Conference on Multimedia Information Processing and Retrieval, pp.1277-1286, 2018.

J. Alex and S. Bernhard, "A tutorial on support vector regression", Statistics and Computing, Vol.14, No.3, pp.199-222, 2004.

G. Huang, H. Zhou, X. Ding, et al., "Extreme learning machine for regression and multiclass classification", IEEE Transactions on Systems, Man, and Cybernetics, Part B:Cybernetics, Vol.42, No.2, pp.513-529, 2012.

S. Pang, M. Wang, S. Qiao, et al., "Fault diagnosis for service composition by spiking neural P systems with colored spikes", Chinese Journal of Electronics, Vol.28, No.5, pp.1033-1040, 2019.

Relative Articles

Supplements(0)

Cited By

Proportional views