Affiliations: School of Arts and Design, Zhuhai College of Science and Technology, Zhuhai, Guangdong 519041, China | E-mail: [email protected]
Correspondence:
[*]
Corresponding author: School of Arts and Design, Zhuhai College of Science and Technology, Zhuhai, Guangdong 519041, China. E-mail: [email protected].
Abstract: 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.
Keywords: Big data, visual communication, color semantics, sequence mining, advertisement click-through rate
