User Consumption Behavior Recognition Based on SMOTE and Improved AdaBoost

Huijuan Hu, Dingju Zhu, Tao Wang, Chao He, Juel Sikder, Yangchun Jia

OAI: oai:igi-global.com:315302 • DOI: 10.4018/IJSSCI.315302

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.