Movie box office prediction based on ensemble learning

Wu, Shuangyan; Zheng, Yufan; Lai, Zhikang; Wu, Fan; Zhan, Choujun

doi:10.1109/ispce-cn48734.2019.8958631

Cited by 3 publications

(1 citation statement)

References 12 publications

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“…Liu and Xie applied bagging to develop a larger dataset for training decision tree models [10]. Wu et al used traditional decision tree algorithms alongside RF and GBDT for model construction, with GBDT showing the best performance [11]. Lee et al confirmed that decision tree-based ensemble methods outperformed both linear regression and other non-ensemble techniques [12].…”

Section: Related Workmentioning

confidence: 99%

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Xie

2024

ACE

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Predicting the revenue of a movie prior to its release presents a significant challenge. The ability to predict pre-release revenue enables movie production companies to devise effective marketing strategies and mitigate the risks associated with potential box office failures. The primary hurdles in this endeavor stem from managing the myriad factors influencing box office outcomes and accurately forecasting a movie's revenue before it becomes available to the public. To overcome these challenges, we introduce a sophisticated Early Movie Box Office Prediction Model that incorporates Ensemble Learning and Feature Encoding techniques. This model amalgamates multiple foundational models, utilizing regression and decision trees to forecast box office revenues. Our composite model demonstrates superior performance over its constituent models, achieving an impressive accuracy rate of 91.4%.

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Section: Related Workmentioning

confidence: 99%

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Xie

2024

ACE

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Revisiting predictions of movie economic success: random Forest applied to profits

Souza

Nishijima

Pires

2023

Multimed Tools Appl

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Previous studies have employed machine learning tools to classify films according to success to guide a reduction in the degree of uncertainty of film production. We revisited the literature to contribute to three relevant issues in classifying films according to economic success. First, we explored the differences between the results of the shortest or longest samples in terms of time to study possible changes in patterns of consumption mainly due to technological changes and between total and wide-released films. Second, we used profits free of price inflation as measures of economic success instead of the usual box office nominal revenues. Third, we employed a smaller set of features, only the ones available at the time of production, to help producers maneuver contingencies since little or nothing can be done by the time a film is in the theaters. We followed the literature to choose the classifiers - Random Forest, Support Vector Machine, and Neural Network - and designed sub-datasets to model and compare the performance of our results. Our dataset includes all films with budgets disclosed at the Box Office Mojo website, resulting in 3167 movies released at theaters worldwide between 1980 and 2019. The Random Forest results outperform previous similar studies with different sampling in time, including results for a less usual larger sample, with the best data sample about 97% both in accuracy and F1-score. Supplementary Information The online version contains supplementary material available at 10.1007/s11042-023-15169-4.

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COVID-19’s Impact on the Box office: Machine Learning and Difference-in-Difference

Zheng

Zhen

Tan

et al. 2021

2021 16th International Conference on Intelligent Systems and Knowledge Engineering (ISKE)

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Movie box office prediction based on ensemble learning

Cited by 3 publications

References 12 publications

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

A refined approach to early movie box office prediction leveraging ensemble learning and feature encoding

Revisiting predictions of movie economic success: random Forest applied to profits

COVID-19’s Impact on the Box office: Machine Learning and Difference-in-Difference

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