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.
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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
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DOI: https://doi.org/10.1007/978-3-319-26532-2_7
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