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Google开源BERT模型源代码.pdf
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文件大小: 467kb
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上传时间: 2020-05-10
详细说明：[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.
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