Explainable Neural Network analysis on Movie Success Prediction

S. Bhavesh Kumar, Sagar Dhanaraj Pande
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Abstract

These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful.  this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.
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电影成功预测的可解释神经网络分析
如今,电影是娱乐业最重要的组成部分之一,在过去,你可以看到每天都有人站在影院门口,或在 OTT 平台上观看电影。但由于工作繁忙,看电影的人并不多。他们会上网搜索评分最高的电影,然后去影院观看。创作一部成功的电影并非易事。本研究将神经网络模型应用于 IMDb 数据集,然后由于其复杂性,为了实现增强分析的局部可解释性和全局可解释性,研究使用了 SHAP(夏普利加法解释)进行分析。
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