Li Yanmin, Zhong Ziqi, Zhang Fengrui, Zhao Xinjie
Pan Tianshou College of Architecture, Art and Design, Ningbo University, Ningbo, China.
Department of Management, The London School of Economics and Political Science, London, United Kingdom.
Front Psychol. 2022 Apr 6;13:784311. doi: 10.3389/fpsyg.2022.784311. eCollection 2022.
In the course of consumer behavior, it is necessary to study the relationship between the characteristics of psychological activities and the laws of behavior when consumers acquire and use products or services. With the development of the Internet and mobile terminals, electronic commerce (E-commerce) has become an important form of consumption for people. In order to conduct experiential education in E-commerce combined with consumer behavior, courses to understand consumer satisfaction. From the perspective of E-commerce companies, this study proposes to use artificial intelligence (AI) image recognition technology to recognize and analyze consumer facial expressions. First, it analyzes the way of human-computer interaction (HCI) in the context of E-commerce and obtains consumer satisfaction with the product through HCI technology. Then, a deep neural network (DNN) is used to predict the psychological behavior and consumer psychology of consumers to realize personalized product recommendations. In the course education of consumer behavior, it helps to understand consumer satisfaction and make a reasonable design. The experimental results show that consumers are highly satisfied with the products recommended by the system, and the degree of sanctification reaches 93.2%. It is found that the DNN model can learn consumer behavior rules during evaluation, and its prediction effect is increased by 10% compared with the traditional model, which confirms the effectiveness of the recommendation system under the DNN model. This study provides a reference for consumer psychological behavior analysis based on HCI in the context of AI, which is of great significance to help understand consumer satisfaction in consumer behavior education in the context of E-commerce.
在消费者行为过程中，研究消费者获取和使用产品或服务时心理活动特征与行为规律之间的关系很有必要。随着互联网和移动终端的发展，电子商务已成为人们重要的消费形式。为了结合消费者行为开展电子商务体验式教育，开设了解消费者满意度的课程。从电子商务公司的角度出发，本研究提出利用人工智能（AI）图像识别技术识别和分析消费者面部表情。首先，分析电子商务背景下的人机交互（HCI）方式，并通过HCI技术获取消费者对产品的满意度。然后，使用深度神经网络（DNN）预测消费者的心理行为和消费心理，以实现个性化产品推荐。在消费者行为课程教育中，有助于了解消费者满意度并进行合理设计。实验结果表明，消费者对系统推荐的产品高度满意，满意度达到93.2%。研究发现，DNN模型在评估过程中能够学习消费者行为规则，其预测效果比传统模型提高了10%，证实了DNN模型下推荐系统的有效性。本研究为人工智能背景下基于HCI的消费者心理行为分析提供了参考，对帮助理解电子商务背景下消费者行为教育中的消费者满意度具有重要意义。
