Computer Science ›› 2020, Vol. 47 ›› Issue (6A): 424-428.doi: 10.11896/JsJkx.190900018

• Database & Big Data & Data Science • Previous Articles     Next Articles

Research on Prediction of Re-shopping Behavior of E-commerce Customers

LV Ze-yu, LI Ji-xuan, CHEN Ru-Jian and CHEN Dong-ming   

  1. Software College,Northeastern University,Shenyang 110167,China
  • Published:2020-07-07
  • About author:LV Ze-yu, born in 1998, postgraduate.His main research interests include machine learning and so on.
    CHEN Dong-ming, born in 1968, Ph.D, professor, Ph.D supervisor, is a member of China Computer Federation.His main research interests include complex networks, social network analysis, machine learning and information security.
  • Supported by:
    This work was supported by the National Training Program of Innovation and Entrepreneurship for Undergraduates (201910145222) and Fundamental Research Funds for the Central Universities(N182410001).

Abstract: The study of customers’ shopping behavior is a trending research topic and has great commercial value for e-commerce companies.This paper studies the prediction of customer’s re-shopping behavior on the same e-commerce platform.Through the analysis of shopping related actions of customers and transaction records between customers and merchants,a variety of different behavior features are designed based on feature engineering principles,and the importance and characteristics of the prediction features are analyzed by using visualization approaches.Then,based on the proposed predictive features,a variety of different algorithms are used to train the prediction models.Experimental research shows that the multi-lightGBM model ensemble method can achieve high prediction accuracy,and the AUC value can reach 0.7018.Meanwhile,the predictor only needs a few features to obtain very good prediction results.The experimental data set studied in this paper is an open source big data collected in real environment,and the research conclusions have both application and academic value.

Key words: Feature engineering, Feature visualization, Model ensemble, Re-shopping behavior prediction

CLC Number: 

  • TP181
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