A Novel Consumer Purchase Behavior Recognition Method Using Ensemble Learning Algorithm

With the prosperous development of e-commerce platforms, consumer returns often occur. The issue of returns has become a stumbling block to the profitability of e-commerce companies. To protect consumers’ purchase rights, the Chinese government has introduced a 7-day unreasonable return policy. In order to use the return policy to attract consumers to buy, various e-commerce platforms have created a more relaxed and convenient return environment for consumers. On the one hand, the introduction of the return policy has increased customer trust in e-commerce platforms and stimulated purchase demand. On the other hand, the return behavior also increases the cost of the e-commerce platform. With the upgrading of consumption, customers pay more attention to personalized experience. In addition to considering price when purchasing online, the quality of services provided by e-commerce platforms will also directly affect customers’ purchasing decisions and return behavior. Therefore, under the personalized return policy of the e-commerce platform, whether consumers will make another purchase is worth studying. In order to achieve this goal, an ensemble learning method (AdaBoost-FSVM) based on fuzzy support vector machine (FSVM) is applied to predict the purchase intention of consumers. First, the grid search method is used to optimize the modeling parameters of the FSVM base classifier. Second, the AdaBoost-FSVM ensemble prediction model is constructed by using multiple base classifiers. In order to evaluate the performance of the prediction models used, logistic regression (LR), support vector machine (SVM), FSVM, random forest (RF), and XGBoost were used to construct prediction models for purchasing behavior. The experimental results demonstrate that the method used in this study has a more accurate prediction effect than the comparison algorithms. The predictive model used in this study can be used in the recommendation system of shopping websites and can also be used to guide e-commerce companies to customize various preferential policies and services, so as to quickly and accurately stimulate the purchase intention of more potential consumers.