Google开源BERT模型源代码.pdf

[Objective] This paper compares the prediction accuracy and efficiency of different machine learning algorithms, aiming to identify new consumers with repeat purchase intentions. It also provides a theoretical framework for customer classification. [Methods] First, we collected the server logs of a dealer on Taobao.com from 2015 to 2018,as well as its orders and consumers’ personal information. And then, we used different algorithms to train theproposedmodels. [Results] The SMOTE algorithm combined with the random forest algorithm obtained the highest prediction accuracy of 96%. [Limitations] The sample data size needs to be expanded. [Conclusions] The fusion algorithm basedon SMOTE and random forest has better performance in predicting repurchase intentions of new consumers.