多分支LSTM用CNN-LSTM编码潜伏特征,用于YouTube的受欢迎程度预测
Neeti Sangwan1, Vishal Bhatnagar2
1GGS Indraprastha University and Maharaja Surajmal Insitute of Technology, New Delhi, India. neetisangwan@gmail.com.
Scientific reports
|January 20, 2025
概括
这项研究引入了一种新的深度学习模型,用于预测YouTube视频的受欢迎程度. 多分支LSTM编码功能与融合深度学习预测器显著提高预测准确性,减少50%的错误.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 数字媒体的增长推动了对引人入胜的视频内容和收入创造的需求.
- 优酷的收入共享模式激励内容创作和受欢迎程度.
- 准确的视频人气预测对于内容创作者和平台来说至关重要.
研究的目的:
- 开发一个有效的深度学习模型来预测YouTube视频的受欢迎程度.
- 探索从视频特征中获得的潜在特征的使用,以改善预测.
- 将拟议模型的性能与现有的预测方法进行比较.
主要方法:
- 一个新的多分支子-家长长期短期记忆 (LSTM) 网络被用来将视频特征映射到一个低维的潜在空间中.
- 然后,这些潜伏特征被用来训练与LSTM融合的卷积神经网络 (CNN),用于普及预测.
- 拟议的模型,称为MLEF-DL预测器,与线性回归 (LR),支向量回归 (SVR) 和使用LSTM的完全卷积网络 (FCN) 进行了评估.
主要成果:
- 该MLEF-DL预测指标在现有方法上显示出显著的改进.
- 实现了50%的平均绝对误差 (MAE) 减少.
- 确定系数 (R2) 增加了0.61%,表明它更好地符合数据.
结论:
- 拟议的多分支LSTM编码功能与融合深度学习预测器 (MLEF-DL预测器) 有效地提高了YouTube视频受欢迎程度的预测.
- 使用新型LSTM架构进行潜在特征提取是提高预测准确性的关键.
- 该模型对视频分析的传统和现有的深度学习方法提供了实质性的进步.
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