Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing: Exploring and Predicting Advertising Effectiveness

Yingna Chao, Hongfeng Zhu, Yueding Zhou

Source Title: Journal of Organizational and End User Computing (JOEUC)36(1)

ISSN: 1546-2234|EISSN: 1546-5012|EISBN13: 9798369324530|DOI: 10.4018/JOEUC.342092

MLA

Chao, Yingna, et al. "Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing: Exploring and Predicting Advertising Effectiveness." JOEUC vol.36, no.1 2024: pp.1-28. http://doi.org/10.4018/JOEUC.342092

APA

Chao, Y., Zhu, H., & Zhou, Y. (2024). Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing: Exploring and Predicting Advertising Effectiveness. Journal of Organizational and End User Computing (JOEUC), 36(1), 1-28. http://doi.org/10.4018/JOEUC.342092

Chicago

Chao, Yingna, Hongfeng Zhu, and Yueding Zhou. "Integrating Visual Transformer and Graph Neural Network for Visual Analysis in Digital Marketing: Exploring and Predicting Advertising Effectiveness," Journal of Organizational and End User Computing (JOEUC) 36, no.1: 1-28. http://doi.org/10.4018/JOEUC.342092

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Abstract

In today's digital economy, digital marketing has become a crucial means for businesses to drive growth and enhance brand exposure. However, with increasing competition, predicting and optimizing advertising effectiveness has become a pivotal component in formulating digital marketing strategies. In order to better understand ad creatives and deeply explore the information within them, this study focuses on integrating visual transformer (VIT) and graph neural network (GNN) methods. Additionally, the study leverages generative adversarial networks (GAN) to enhance the quality of visual features, aiming to achieve visual analysis, exploration, and prediction of advertising effectiveness in digital marketing. This approach begins by employing VIT, an emerging visual transformer technology, to transform image information into high-dimensional feature representations.