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Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education

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Listed:
  • Li, Yanmin
  • Zhong, Ziqi
  • Zhang, Fengrui
  • Zhao, Xinjie

Abstract

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.

Suggested Citation

  • Li, Yanmin & Zhong, Ziqi & Zhang, Fengrui & Zhao, Xinjie, 2022. "Artificial intelligence-based human–computer interaction technology applied in consumer behavior analysis and experiential education," LSE Research Online Documents on Economics 115047, London School of Economics and Political Science, LSE Library.
    • Handle: RePEc:ehl:lserod:115047
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    References listed on IDEAS

    as
    1. Kim, Tae-Young & Cho, Sung-Bae, 2019. "Predicting residential energy consumption using CNN-LSTM neural networks," Energy, Elsevier, vol. 182(C), pages 72-81.
    2. Kim, Jina & Ji, HongGeun & Oh, Soyoung & Hwang, Syjung & Park, Eunil & del Pobil, Angel P., 2021. "A deep hybrid learning model for customer repurchase behavior," Journal of Retailing and Consumer Services, Elsevier, vol. 59(C).
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    More about this item

    Keywords

    behavior analysis; customer psychology; deep neutral network; human-computer interaction; image recognition;
    All these keywords.

    JEL classification:

    • L81 - Industrial Organization - - Industry Studies: Services - - - Retail and Wholesale Trade; e-Commerce

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