Research on prediction of e-commerce repurchase behavior based on multiple fusion models

Abstract

With the advent of the Internet era, online shopping has become an integral part of peoples life. In order to perform precision marketing, more and more e-commerce platforms are trying to predict users repurchase behaviors by collecting massive user behavior data. Although the traditional single-model prediction method is mature, it is still difficult to improve the accuracy of prediction. Based on the real user behavior data of Tmall, this paper focuses on comparing and exploring the help of different algorithm fusion methods to improve the model prediction effect. The under-sampling method is introduced for sample equalization processing. User behavior features are constructed from three aspects which are user, merchant and user-merchant interaction. Taking AUC value as evaluation method, Soft-Voting and Stacking model fusion methods are used to integrate logistics regression, KNN, XGBoost and RandomForest. And the prediction results is produced based on stratified 5-fold cross-validation. The experimental results show that the fusion model can effectively improve the prediction effect, and the AUC value is raised by 0.2%~4% compared with the single model. The AUC value of Soft-Voting increases by approximately 0.4% after it is weighted.