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基于预训练–微调策略的电影票房预测
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Abstract:
有监督学习模型对数据量有着较高的依赖，然而现有电影票房数据集较少，导致预测精度降低。针对上述问题，提出一种基于预训练–微调策略的电影票房预测模型。利用电影评分和电影票房之间的相关性，在电影评分数据集上采用预训练的方式，使模型提前获取有关电影的先验知识，同时利用电影间的属性差异信息进行数据增强，最后在电影票房数据集上进行微调，实现对电影票房的预测。实验结果表明，所提方法R2指标提升了7%，MSE下降了69%。
Supervised learning models have a high dependence on the amount of data, however, the existing movie box office dataset is small, which leads to lower prediction accuracy. To address the above problems, a movie box office prediction model based on a pre-training and fine-tuning strategy is proposed. Using the correlation between movie ratings and movie box office, pre-training is used on the movie ratings dataset to make the model acquire a priori knowledge about movies in advance. At the same time, data enhancement is carried out by using the information of attribute differences between movies. Finally fine-tuning is applied on the movie box office dataset to realize the predic-tion of movie box office. Experimental results show that the proposed method improves the R2 in-dex by 7% and decreases the MSE by 69%.
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