在使用DeepFM和长短期记忆 (LSTM) 的动态交互场景中设计广告.
Lingling Zeng1, Muhammad Asif2
1Art College, Xinxiang Engineering College, Xinxiang, Henan, China.
PeerJ. Computer science
|April 25, 2024
概括
这项研究引入了一种新的数据驱动的神经网络方法,以提高在线媒体中电影和电视广告的准确性. 先进的模型提高了动态视觉广告内容的实时评估精度.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 媒体研究 媒体研究
背景情况:
- 传统的电影和电视广告评估方法在新媒体环境中面临准确性限制.
- 基于网络的媒体对广告数据分析提出了动态和交互性的挑战.
研究的目的:
- 提出一种新的数据驱动的非线性动态神经网络规划方法.
- 提高网络媒体中电影和电视广告的实时评估精度和准确性.
主要方法:
- 为动态交互式网络媒体环境开发了视觉广告设计模型.
- 在深度学习神经网络中利用了DeepFM和长短期记忆 (LSTM) 模块.
- 使用两个公共数据集构建了一个信息统计和数据兴趣模型,并为视觉广告设计了功能.
主要成果:
- 拟议的视觉神经网络模型在捕捉非线性动态和特征挖掘方面表现出卓越的性能.
- 与使用F1和根平均平方误差 (RMSE) 度量的其他模型进行基准测试证实了该模型的有效性.
- 该模型在处理视觉元素和文本之间的动态交互方面表现出了强度和概括能力.
结论:
- 数据驱动的神经网络方法显著提高了互动在线媒体中的广告评估准确性.
- 该模型能够处理复杂的视觉和文本数据动态,为现代广告挑战提供了强大的解决方案.
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