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Predicting movie success with machine learning techniques: ways to improve accuracy

Author

Listed:
  • Kyuhan Lee

    (Seoul National University)

  • Jinsoo Park

    (Seoul National University)

  • Iljoo Kim

    (Saint Joseph’s University)

  • Youngseok Choi

    (Brunel University)

Abstract

Previous studies on predicting the box-office performance of a movie using machine learning techniques have shown practical levels of predictive accuracy. Their works are technically- and methodologically-oriented, focusing mainly on what algorithms are better at predicting the movie performance. However, the accuracy of prediction model can also be elevated by taking other perspectives such as introducing unexplored features that might be related to the prediction of the outcomes. In this paper, we examine multiple approaches to improve the performance of the prediction model. First, we develop and add a new feature derived from the theory of transmedia storytelling. Such theory-driven feature selection not only increases the forecast accuracy, but also enhances the interpretability of a prediction model. Second, we use an ensemble approach, which has rarely been adopted in the research on predicting box-office performance. As a result, the proposed model, Cinema Ensemble Model (CEM), outperforms the prediction models from the past studies that use machine learning algorithms. We suggest that CEM can be extensively used for industrial experts as a powerful tool for improving decision-making process.

Suggested Citation

  • Kyuhan Lee & Jinsoo Park & Iljoo Kim & Youngseok Choi, 0. "Predicting movie success with machine learning techniques: ways to improve accuracy," Information Systems Frontiers, Springer, vol. 0, pages 1-12.
  • Handle: RePEc:spr:infosf:v::y::i::d:10.1007_s10796-016-9689-z
    DOI: 10.1007/s10796-016-9689-z
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    References listed on IDEAS

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    3. Hamed M. Zolbanin & Dursun Delen & Durand Crosby & David Wright, 0. "A Predictive Analytics-Based Decision Support System for Drug Courts," Information Systems Frontiers, Springer, vol. 0, pages 1-20.
    4. Jong-Min Kim & Leixin Xia & Iksuk Kim & Seungjoo Lee & Keon-Hyung Lee, 2020. "Finding Nemo: Predicting Movie Performances by Machine Learning Methods," JRFM, MDPI, vol. 13(5), pages 1-12, May.

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