This article addresses the evolving landscape of data advertising within network-based new media, seeking to mitigate the accuracy limitations prevalent in traditional film and television advertising evaluations. To overcome these challenges, a novel data-driven nonlinear dynamic neural network planning approach is proposed. Its primary objective is to augment the real-time evaluation precision and accuracy of film and television advertising in the dynamic interactive realm of network media. The methodology primarily revolves around formulating a design model for visual advertising in film and television, customized for the dynamic interactive milieu of network media. Leveraging DeepFM+long short-term memory (LSTM) modules in deep learning neural networks, the article embarks on constructing a comprehensive information statistics and data interest model derived from two public datasets. It further engages in feature engineering for visual advertising, crafting self-learning association rules that guide the data-driven design process and system flow. The article concludes by benchmarking the proposed visual neural network model against other models, using F1 and root mean square error (RMSE) metrics for evaluation. The findings affirm that the proposed model, capable of handling dynamic interactions among images, visual text, and more, excels in capturing nonlinear and feature-mining aspects. It exhibits commendable robustness and generalization capabilities within various contexts.

中文翻译：

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使用 DeepFM 和长短期记忆（LSTM）进行动态交互场景的广告设计

本文探讨了基于网络的新媒体中数据广告不断发展的格局，试图减轻传统电影和电视广告评估中普遍存在的准确性限制。为了克服这些挑战，提出了一种新颖的数据驱动的非线性动态神经网络规划方法。其主要目标是提高网络媒体动态交互领域影视广告的实时评估精度和准确性。该方法主要围绕制定电影和电视视觉广告的设计模型，为网络媒体的动态交互环境定制。本文利用深度学习神经网络中的 DeepFM+长短期记忆（LSTM）模块，着手构建基于两个公共数据集的综合信息统计和数据兴趣模型。它还进一步从事视觉广告的特征工程，制定自学习关联规则来指导数据驱动的设计过程和系统流程。本文最后将所提出的视觉神经网络模型与其他模型进行基准测试，使用 F1 和均方根误差 (RMSE) 指标进行评估。研究结果证实，所提出的模型能够处理图像、视觉文本等之间的动态交互，在捕获非线性和特征挖掘方面表现出色。它在各种上下文中表现出值得称赞的稳健性和泛化能力。