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

Predicting movie success with machine learning techniques: ways to improve accuracy
Predicting movie success with machine learning techniques: ways to improve accuracy
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.
Cinema ensemble model, Feature selection, Machine learning techniques, Movie performance, Prediction model, Transmedia storytelling
1572-9419
577-588
Lee, Kyuhan
7317ace5-3318-458e-bdb9-a59ca38653a1
Park, Jinsoo
f7c18d81-7513-42ca-880e-cfca18c9177c
Kim, Iljoo
36ba0f3b-7f93-4a17-be71-8db34ac05ff8
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106
Lee, Kyuhan
7317ace5-3318-458e-bdb9-a59ca38653a1
Park, Jinsoo
f7c18d81-7513-42ca-880e-cfca18c9177c
Kim, Iljoo
36ba0f3b-7f93-4a17-be71-8db34ac05ff8
Choi, Youngseok
928c489e-7c5b-42fc-bad8-77ce717ba106

Lee, Kyuhan, Park, Jinsoo, Kim, Iljoo and Choi, Youngseok (2018) Predicting movie success with machine learning techniques: ways to improve accuracy. Information Systems Frontiers, 20 (3), 577-588. (doi:10.1007/s10796-016-9689-z).

Record type: Article

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.

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More information

Accepted/In Press date: 1 April 2016
e-pub ahead of print date: 19 August 2016
Published date: June 2018
Keywords: Cinema ensemble model, Feature selection, Machine learning techniques, Movie performance, Prediction model, Transmedia storytelling

Identifiers

Local EPrints ID: 437728
URI: http://eprints.soton.ac.uk/id/eprint/437728
ISSN: 1572-9419
PURE UUID: 26aa9dc7-74b7-49de-bb7e-c92413bb2dfe
ORCID for Youngseok Choi: ORCID iD orcid.org/0000-0001-9842-5231

Catalogue record

Date deposited: 13 Feb 2020 17:30
Last modified: 16 Sep 2021 11:14

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Contributors

Author: Kyuhan Lee
Author: Jinsoo Park
Author: Iljoo Kim
Author: Youngseok Choi ORCID iD

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