Abstract
Predicting box office receipts of movies in theatres is a difficult and challenging problem on which many theatre managers cogitated. In this study, we use pruned random forest to predict the box office of the first week in Chinese theatres one month before movies’ theatrical release. In our model, the prediction problem is converted into a classification problem, where the box office receipt of a movie is discretized into eight categories. Experiments on 68 theatres show that the proposed method outperforms other statistical models. In fact, our model can predict the expected revenue range of a movie, it can be used as a powerful decision aid by theatre managers.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Ainslie, A., Drèze, X., Zufryden, F.: Modeling movie life cycles and market share. Mark. Sci. 24(3), 508–517 (2005)
Breiman, L.: Prediction games and arcing algorithms. Neural Comput. 11(7), 1493–1517 (1999)
Breiman, L.: Random forests. Mach. Learn. 45(1), 5–32 (2001)
Eliashberg, J., Elberse, A., Leenders, M.A.: The motion picture industry: critical issues in practice, current research, and new research directions. Mark. Sci. 25(6), 638–661 (2006)
Hennig-Thurau, T., Houston, M.B., Walsh, G.: Determinants of motion picture box office and profitability: an interrelationship approach. Rev. Manag. Sci. 1(1), 65–92 (2007)
Lee, K.J., Chang, W.: Bayesian belief network for box-office performance: a case study on korean movies. Expert Syst. Appl. 36(1), 280–291 (2009)
Liaw, A., Wiener, M.: Classification and regression by randomforest. R News 2(3), 18–22 (2002)
Martınez-Munoz, G., Suárez, A.: Aggregation ordering in bagging. In: Proceedings of the IASTED International Conference on Artificial Intelligence and Applications, pp. 258–263. Citeseer (2004)
Martinez-Muoz, G., Hernández-Lobato, D., Suárez, A.: An analysis of ensemble pruning techniques based on ordered aggregation. IEEE Trans. Pattern Anal. Mach. Intell. 31(2), 245–259 (2009)
Panaligan, R., Chen, A.: Quantifying movie magic with google search. Google WhitepaperIndustry Perspectives+ User Insights (2013)
Rätsch, G., Onoda, T., Müller, K.R.: Soft margins for adaboost. Mach. Learn. 42(3), 287–320 (2001)
Sharda, R., Delen, D.: Predicting box-office success of motion pictures with neural networks. Expert Syst. Appl. 30(2), 243–254 (2006)
Shen, C., Li, H.: Boosting through optimization of margin distributions. IEEE Trans. Neural Netw. 21(4), 659–666 (2010)
Yang, F., Lu, W.H., Luo, L.K., Li, T.: Margin optimization based pruning for random forest. Neurocomputing 94, 54–63 (2012)
Zhang, H., Wang, M.: Search for the smallest random forest. Stat. Interface 2(3), 381 (2009)
Zhang, L., Luo, J., Yang, S.: Forecasting box office revenue of movies with bp neural network. Expert Syst. Appl. 36(3), 6580–6587 (2009)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Guo, Z., Zhang, X., Hou, Y. (2015). Predicting Box Office Receipts of Movies with Pruned Random Forest. In: Arik, S., Huang, T., Lai, W., Liu, Q. (eds) Neural Information Processing. ICONIP 2015. Lecture Notes in Computer Science(), vol 9489. Springer, Cham. https://doi.org/10.1007/978-3-319-26532-2_7
Download citation
DOI: https://doi.org/10.1007/978-3-319-26532-2_7
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26531-5
Online ISBN: 978-3-319-26532-2
eBook Packages: Computer ScienceComputer Science (R0)