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DeepFusion: predicting movie popularity via cross-platform feature fusion

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Abstract

For online video service providers, the accurate prediction of video popularity directly impacts their advertisement revenue, bandwidth provisioning policy and copyright procurement decision. Most of previous approaches only utilize data from a single platform (e.g., view history) for prediction. However, such approaches cannot provide satisfactory prediction accuracy, as video popularity may be affected by many influential features dispersed over multiple platforms. In this paper, we focus on the popularity prediction of online movies and propose a prediction framework called DeepFusion to fuse salient features from multiple platforms so as to boost the accuracy of popularity prediction of online movies. For this purpose, we extract influential factors from Douban, which is a leading movie rating website in China, and Youku, which is one of the largest online video service providers in China. Considering the complexity incurred by numerous parameters, we choose to feed these influential factors into deep neural networks for prediction and thus avoid the limitation of traditional predictive models. Compared with previous approaches, our solution can significantly improve the prediction accuracy over 40%. Moreover, even for movies without any historical views, our approach can also well capture their popular trends and overcome the cold-start problem.

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Notes

  1. https://www.statista.com/topics/842/netflix/

  2. http://report.iresearch.cn/

  3. For convenience, we use the term movie interchangeably with video and film in the rest content.

  4. https://www.douban.com/

  5. http://www.youku.com/

  6. http://www.imdb.com

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Acknowledgments

This work was supported by the National Natural Science Foundation of China under Grant U1911201, Guangdong Special Support Program under Grant2017TX04X148, the Fundamental Research Funds for the Central Universities under Grant19LGZD37 and 19LGYJS57, and ARC DE180100950.

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Authors and Affiliations

  1. School of Data and Computer Science, Sun Yat-Sen University, Guangzhou, China

    Wen Bai, Yuxiao Zhang, Weiwei Huang & Di Wu

  2. Collaborative Innovation Center of High Performance Computing, Sun Yat-Sen University, Guangzhou, China

    Wen Bai & Di Wu

  3. Department of Computing, Macquarie University, Sydney, Australia

    Yipeng Zhou

  4. Guangdong Key Laboratory of Big Data Analysis and Processing, Sun Yat-Sen University, Guangzhou, China

    Di Wu

  5. College of Computer Science and Software Engineering, Shenzhen University, Shenzhen, China

    Gang Liu

  6. Department of Communication Engineering, Xiamen University, Fujian, China

    Liang Xiao

Authors
  1. Wen Bai
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  2. Yuxiao Zhang
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  3. Weiwei Huang
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  4. Yipeng Zhou
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  5. Di Wu
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  6. Gang Liu
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  7. Liang Xiao
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Correspondence to Di Wu.

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Bai, W., Zhang, Y., Huang, W. et al. DeepFusion: predicting movie popularity via cross-platform feature fusion. Multimed Tools Appl 79, 19289–19306 (2020). https://doi.org/10.1007/s11042-020-08730-y

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  • DOI: https://doi.org/10.1007/s11042-020-08730-y

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