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Electronic Markets

, Volume 29, Issue 2, pp 263–274 | Cite as

User behaviour modeling, recommendations, and purchase prediction during shopping festivals

  • Ming Zeng
  • Hancheng Cao
  • Min Chen
  • Yong LiEmail author
Research Paper

Abstract

This work investigates user online browsing and purchasing behaviors, and predicts purchasing actions during a large shopping festival in China. To improve online shopping experience for consumers, increase sales for merchants and achieve effective warehousing and delivery, we first analyse diverse online shopping behaviours based on the 31 million logs generated accompanied with online shopping during a rushed sale event on 11st November, 2016. Based on the obtained user behaviours and massive data, we apply collaborative filtering based method to recommend items for different consumers, and predict whether purchase will happen. We conduct 5-fold cross validation to evaluate the collaborative filtering based recommendation method, and further identify the critical shopping behaviors that determine the precursors of purchases. As online shopping becomes a global phenomenon, findings in this study have implications on both shopping experience and sales enhancement.

Keywords

Online shopping Behavior analyse Recommendations Purchase prediction Collaborative filtering 

JEL Classifications

D12 

Notes

Acknowledgements

Prof. Min Chen's work was supported by Diretor Fund of WNLO.

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Copyright information

© Institute of Applied Informatics at University of Leipzig 2018

Authors and Affiliations

  1. 1.Department of Electronic EngineeringTsinghua UniversityBeijingChina
  2. 2.Wuhan National Laboratory for Optoelectronics, School of Computer Science and TechnologyHuazhong University of Science and TechnologyWuhanChina

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