User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

Huijuan Hu, Dingju Zhu, Tao Wang, Chao He, Juel Sikder, Yangchun Jia
DOI: 10.4018/IJSSCI.315302
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Abstract

The sudden outbreak of COVID-19 has dealt a huge blow to traditional education and training companies. Institutions use the WeChat platform to attract users, but how to identify high-quality users has always been a difficult point for enterprises. In this paper, researchers proposed a classification algorithm based on SMOTE and the improved AdaBoost, which fuses feature information weights and sample weights to effectively solve the problems of overfitting and sample imbalance. To justify the study, it was compared with other traditional machine-learning algorithms. The accuracy and recall of the model increased by 19% and 36%, respectively, and the AUC value reached 0.98, indicating that the model could effectively identify the user's purchase intention. The proposed algorithm also ensures that it works well in spam identification and fraud detection. This research is of great significance for educational institutions to identify high-quality users of the WeChat platform and increase purchase conversion rate.
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Introduction

The impact of the COVID-19 pandemic on the education and training industry has two aspects: On the one hand, it has accelerated the development of online education, integrated online education resources, and accelerated the development of online education. On the other hand, traditional education and training institutions adopted offline teaching modes and traditional enrollment methods, which have been greatly impacted by the pandemic. Traditional enrollment methods (such as flyers and billboards) have limited information delivery. WeChat marketing is undoubtedly one of the most popular enrolling tools for educational institutions. WeChat has a huge number of users; thus, many people can receive information through WeChat. However, this is a group composed of different people with different needs. If there is no market segmentation, there is no choice of their own target market, and there are no targeted measures for different users. This will cause problems such as a low transforming rate of consumption and serious user churn (Fan & Xu, 2017, p. 145).

The continuous development of online education technology has made online learning platforms one of the most important channels for acquiring knowledge. With the continuous improvement of user consumption awareness, more and more scholars are focusing their research perspectives on the study of payment behaviors of online learning platforms. Dodds et al. (1991) argued that the willingness to consume not only reflects the subjective attitude of consumers but also reflects the influence of external factors on consumers’ personal purchasing decisions. Y. Li (2016) constructed a model of influencing factors of online education platform users’ willingness to pay for courses based on related theories, such as perceived value and perceived risk. The study showed that course audition experience, perceived usefulness, perceived value, perceived trust, and perceived risk were the key factors that directly affected users’ willingness to pay for courses. Jia et al. (2018) analyzed the influencing factors of learners’ payment behaviors for commercial online courses and found that the authority of the course offering institution, the simple course name, the high-priced, high-quality courses, and the good course service will significantly promote the payment behavior of learners.

Most of the current research on users’ willingness to pay for knowledge is based on models such as perceived value theory; the factors are complicated, and there is no unified conclusion so far. Therefore, Yan et al. (2021) conducted a quantitative meta-analysis through 29 relevant domestic and foreign empirical studies, systematically sorted out and evaluated the influence of related factors and achieved a comprehensive understanding of users’ online knowledge payment behavior. Research by Ren et al. (2020) showed that in order to achieve sustainable development of online knowledge payment platforms in the future, in addition to understanding the influencing factors such as user behavioral attitudes and user psychological characteristics, it was also necessary to accurately understand user payment intention and continuous payment willingness. However, previous studies have paid more attention to influencing factors and did not divide users.

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