广告标志色彩设计的应用为大数据和视觉传达技术

Huan Tian
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引用次数: 0

摘要

色彩是平面广告的三大要素之一,不同的色彩组合可以引发人类不同的情感体验。目前,色彩在中国广告中的应用相对成熟,但仅限于传统的应用方式,尚未与大数据技术相结合。本研究从业务需求的角度,从业务增值的角度分析视觉创意的过程,并分析大数据在其中的作用。然后介绍了常见颜色的语义以及如何将色彩语义融入广告设计中。提出了一种基于序列挖掘的广告点击率预测模型。使用Criteo数据集作为训练集。模型的AUC值为0.702,loss值为0.415。与其他模型相比,AUC值分别增加了10.16%、4.70%、2.69%和2.30%。损失分别下降10.17%、9.19%、6.11%和7.57%。最后,以20位消费者的网购数据作为测试集,预测其色彩偏好,预测准确率约为70%。其中,购物习惯稳定的群体预测准确率为72.76%,喜欢尝试新事物的群体预测准确率为70.60%,均符合预期。通过实验表明,该模型具有良好的性能和稳定性,能够更准确地判断消费者的消费偏好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The application of advertising logo color design for big data and visual communication technology
Color is one of the three major elements of print advertising, and different color combinations can trigger different emotional experiences of human beings. At present, the application of color in advertising in China is relatively mature, but it is limited to the traditional application method and has not been combined with big data technology. From the perspective of business needs, this research analyzes the process of visual creativity from the perspective of business value-added, and analyzes the role of big data in it. Then it introduces the semantics of common colors and how to incorporate color semantics into advertising design. And a sequence mining-based advertising click-through rate prediction model is proposed. The Criteo dataset is used as the training set. The AUC value of the model is 0.702 and the loss value is 0.415. Compared with other models, AUC values increased by 10.16%, 4.70%, 2.69% and 2.30%, respectively. Losses decreased by 10.17%, 9.19%, 6.11% and 7.57%, respectively. Finally, the online shopping data of 20 consumers was used as the test set to predict their color preferences, and the prediction accuracy was about 70%. Among them, the prediction accuracy of the group with stable shopping habits was 72.76%, and that of the group who liked to try new things was 70.60%, both meeting the expectation. Through experiments, it is concluded that the model has good performance and stability, and can more accurately judge consumers’ consumption preferences.
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