Prediction of Online Consumers’Repeat Purchase Behavior via BERT-MLP Model  

作　　者：Junchao Dong Tinghui Huang Liang Min Wenyan Wang 

机构地区：[1]School of Computer Engineering,Guilin University of Electronic Technology,Beihai 536000,Guangxi Province,China [2]School of Computer Science and Information Security,Guilin University of Electronic Technology,Guilin 541004,Guangxi Province,China [3]Department of Computer Science and Technology,Xi’an Jiaotong University City College,Xi’an 710018,Shaanxi Province,China [4]School of Computer Sciences,Universiti Sains Malaysia,Gelugor 11800,Penang,Malaysia

出　　处：《Journal of Electronic Research and Application》2022年第3期12-19,共8页电子研究与应用

基　　金：Shaanxi Provincial Education Science Regulations“Fourteenth Five-Year Plan”Project“Research on the Application of Educational Data Mining in Applied Undergraduate Teaching:A Case Study of‘Computer Application Technology’Course”(Project Number:SGH21Y0403);The 2020 Bureau of Shaanxi Provincial Sports Regular Project(Project Number:2021392);The Special Research Project of Xi’an Jiaotong University City College(Project Number:KCSZ01005)。

摘　　要：It is an effective means for merchants to carry out precision marketing and improve ROI by using historical user behavior data obtained from promotional activities in order to build a model to predict the repeat purchase behavior of users after promotional activities.Most of the existing prediction models are supervised learning,which does not work well with a small amount of labeled data.This paper proposes a BERT-MLP prediction model that uses“large-scale data unsupervised pre-training+small amount of labeled data fine-tuning.”The experimental results on Alibaba real dataset show that the accuracy of the BERT-MLP model is better than the baseline model.

关 键 词：Data mining Business intelligence E-COMMERCE BERT Multilayer perceptron 

分 类 号：TP1[自动化与计算机技术—控制理论与控制工程]