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Predicting movie box-office revenues using deep neural networks

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Abstract

In the film industry, the ability to predict a movie’s box-office revenues before its theatrical release can decrease its financial risk. However, accurate predictions are not easily obtained. The complex relationship between movie-related data and movie box-office revenues, plus the increasing volume of data in online movie databases, pose challenges for their effective analysis. In this paper, a multimodal deep neural network, incorporating input about movie poster features learned in a data-driven fashion, is proposed for movie box-office revenues prediction. A convolutional neural network (CNN) is built to extract features from movie posters. By pre-training the CNN, features that are relevant to movie box-office revenues can be learned. To evaluate the performance of the proposed multimodal deep neural network, comparative studies with other prediction techniques were carried out on an Internet Movie Database dataset, and visualization of movie poster features was also performed. Experimental results demonstrate the superiority of the proposed multimodal deep neural network for movie box-office revenues prediction.

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References

  1. Ainslie A, Drèze X, Zufryden F (2005) Modeling movie life cycles and market share. Mark Sci 24(3):508–517

    Article  Google Scholar 

  2. Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798–1828

    Article  Google Scholar 

  3. Brewer SM, Kelley JM, Jozefowicz JJ (2009) A blueprint for success in the us film industry. Appl Econ 41(5):589–606

    Article  Google Scholar 

  4. Chen T, Li M, Li Y, Lin M, Wang N, Wang M, Xiao T, Xu B, Zhang C, Zhang Z (2015) Mxnet: a flexible and efficient machine learning library for heterogeneous distributed systems. arXiv preprint arXiv:1512.01274

  5. Cortes C, Vapnik V (1995) Support-vector networks. Mach Learn 20(3):273–297

    MATH  Google Scholar 

  6. Deng J, Dong W, Socher R, Li LJ, Li K, Fei-Fei L (2009) Imagenet: a large-scale hierarchical image database. In: IEEE conference on computer vision and pattern recognition, 2009. CVPR 2009. IEEE, pp 248–255

  7. Elberse A (2007) The power of stars: do star actors drive the success of movies? J Mark 71(4):102–120

    Article  Google Scholar 

  8. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  9. Ghiassi M, Lio D, Moon B (2015) Pre-production forecasting of movie revenues with a dynamic artificial neural network. Expert Syst Appl 42(6):3176–3193

    Article  Google Scholar 

  10. Graves A, Mohamed AR, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 6645–6649

  11. Guo Q, Jia J, Shen G, Zhang L, Cai L, Yi Z (2016) Learning robust uniform features for cross-media social data by using cross autoencoders. Knowl Based Syst 102:64–75

    Article  Google Scholar 

  12. He K, Zhang X, Ren S, Sun J (2015) Delving deep into rectifiers: surpassing human-level performance on imagenet classification. In: Proceedings of the IEEE international conference on computer vision (ICCV), pp 1026–1034

  13. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

  14. Hinton GE, Salakhutdinov RR (2006) Reducing the dimensionality of data with neural networks. Science 313(5786):504–507

    Article  MathSciNet  MATH  Google Scholar 

  15. Ivasic-Kos M, Pobar M, Mikec L (2014) Movie posters classification into genres based on low-level features. In: 37th international convention on information and communication technology, electronics and microelectronics (MIPRO), 2014. IEEE, pp 1198–1203

  16. Kim T, Hong J, Kang P (2015) Box office forecasting using machine learning algorithms based on SNS data. Int J Forecast 31(2):364–390

    Article  Google Scholar 

  17. Kingma D, Ba J (2014) Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980

  18. Krizhevsky A, Sutskever I, Hinton GE (2012) Imagenet classification with deep convolutional neural networks. In: Proceedings of the advances in neural information processing systems (NIPS), pp 1097–1105

  19. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  20. LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278–2324

    Article  Google Scholar 

  21. Oh S, Ahn J, Baek H (2015) Viewer engagement in movie trailers and box office revenue. In: 48th hawaii international conference on system sciences (HICSS), 2015. IEEE, pp 1724–1732

  22. Qin L (2011) Word-of-blog for movies: a predictor and an outcome of box office revenue? J Electron Commer Res 12(3):187

    Google Scholar 

  23. Sharda R, Delen D (2006) Predicting box-office success of motion pictures with neural networks. Expert Syst Appl 30(2):243–254

    Article  Google Scholar 

  24. Sutskever I, Vinyals O, Le QV (2014) Sequence to sequence learning with neural networks. In: Proceedings of the advances in neural information processing systems (NIPS), pp 3104–3112

  25. Svetnik V, Liaw A, Tong C, Culberson JC, Sheridan RP, Feuston BP (2003) Random forest: a classification and regression tool for compound classification and QSAR modeling. J Chem Inf Comput Sci 43(6):1947–1958

    Article  Google Scholar 

  26. Tadimari A, Kumar N, Guha T, Narayanan SS (2016) Opening big in box office? Trailer content can help. In: 2016 IEEE international conference on acoustics, speech and signal processing (ICASSP). IEEE, pp 2777–2781

  27. Wang J, Zhang L, Guo Q, Yi Z (2017) Recurrent neural networks with auxiliary memory units. IEEE Trans Neural Netw Learn Syst. doi:10.1109/TNNLS.2017.2677968

  28. Zhang H, Cao X, Ho JK, Chow TW (2016) Object-level video advertising: an optimization framework. IEEE Trans Ind Inf 13(2):520–531

    Article  Google Scholar 

  29. Zhang H, Ji Y, Li J, Ye Y (2016) A triple wing harmonium model for movie recommendation. IEEE Trans Ind Inf 12(1):231–239

    Article  Google Scholar 

  30. Zhang H, Li J, Ji Y, Yue H (2016) Understanding subtitles by character-level sequence-to-sequence learning. IEEE Trans Ind Inf 13(2):616–624

    Article  Google Scholar 

  31. Zhang L, Luo J, Yang S (2009) Forecasting box office revenue of movies with BP neural network. Expert Syst Appl 36(3):6580–6587

    Article  Google Scholar 

  32. Zhou B, Khosla A, Lapedriza A, Oliva A, Torralba A (2016) Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR), pp 2921–2929

  33. Zhou H, Hermans T, Karandikar AV, Rehg JM (2010) Movie genre classification via scene categorization. In: Proceedings of the 18th ACM international conference on multimedia. ACM, pp 747–750

  34. Zhou JT, Pan SJ, Tsang IW, Yan Y (2014) Hybrid heterogeneous transfer learning through deep learning. In: AAAI, pp 2213–2220

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Acknowledgements

This work was supported by the National Science Foundation of China (Grant Numbers 61432012, U1435213).

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Authors and Affiliations

  1. Machine Intelligence Laboratory, College of Computer Science, Sichuan University, Chengdu, 610065, People’s Republic of China

    Yao Zhou, Lei Zhang & Zhang Yi

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  1. Yao Zhou
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  2. Lei Zhang
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  3. Zhang Yi
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Correspondence to Zhang Yi.

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Zhou, Y., Zhang, L. & Yi, Z. Predicting movie box-office revenues using deep neural networks. Neural Comput & Applic 31, 1855–1865 (2019). https://doi.org/10.1007/s00521-017-3162-x

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  • DOI: https://doi.org/10.1007/s00521-017-3162-x

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