在巨额投资之前预测动漫的受欢迎程度:使用深度学习的多式联络方法
Jesús Armenta-Segura1, Grigori Sidorov1
1Centro de Investigación en Computación, Instituto Politécnico Nacional, Mexico City, Mexico City, Mexico.
PeerJ. Computer science
|June 26, 2025
概括
预测动漫的流行对于日本产业来说至关重要. 一个新的数据集和深度学习模型 (GPT-2/ResNet-50) 显示,投资前的特征是相关的,但不是决定成功的.
科学领域:
- 人工智能的人工智能
- 数据科学数据科学数据科学
- 娱乐行业分析 娱乐行业分析
背景情况:
- 预测娱乐产品的成功,特别是动漫,对日本产业至关重要.
- 现有的方法通常需要在大量投资后才能获得数据,从而限制了早期决策.
- 需要一个全面的数据集和强大的模型,利用预投资,公开可用的数据.
研究的目的:
- 引入一种新的,全面的数据集,仅使用预投资特征来预测动漫的受欢迎程度.
- 提出和评估一个深度神经网络 (DNN) 架构用于动漫人气预测.
- 评估早期特征对预测动漫成功的范围和影响.
主要方法:
- 使用自由可用的互联网数据开发一个全面的数据集,遵守现实世界的标准.
- 实现了结合GPT-2 (用于语言理解) 和ResNet-50 (用于图像分析) 的深度神经网络架构.
- 使用平均平方误差 (MSE),R平方 (R2),皮尔森和斯皮尔曼相关系数对模型的评估,与传统基准相比较.
主要成果:
- 拟议的DNN模型实现了0.012的平均平方误差 (MSE),比基准值的0.415.5显著改善.
- 该车型的R平方 (R2) 得分为0.142,大大超过了基准的-37.591.1.
- 皮尔森 (0.382) 和斯皮尔曼 (0.362) 相关系数表明了投资前特征的相关性,但不是决定性.
结论:
- 预投资特征虽然很重要,但本身就不足以最终预测动漫的受欢迎程度.
- 拟议的多式联网深度学习方法为娱乐行业提供了一个有前途的工具,以减轻财务风险.
- 进一步的研究应该探索这些特征与投资后数据的相互作用,以获得更准确的预测.
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