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

Author

Listed:
  • Ming Zeng

    (Tsinghua University)

  • Hancheng Cao

    (Tsinghua University)

  • Min Chen

    (Huazhong University of Science and Technology)

  • Yong Li

    (Tsinghua University)

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.

Suggested Citation

  • Ming Zeng & Hancheng Cao & Min Chen & Yong Li, 2019. "User behaviour modeling, recommendations, and purchase prediction during shopping festivals," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 263-274, June.
    • Handle: RePEc:spr:elmark:v:29:y:2019:i:2:d:10.1007_s12525-018-0311-8
    DOI: 10.1007/s12525-018-0311-8
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    Citations

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    Cited by:

    1. Yin Zhang & Haider Abbas & Yi Sun, 2019. "Smart e-commerce integration with recommender systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 29(2), pages 219-220, June.
    2. Li Li & Xiaotong Li & Wenmin Qi & Yue Zhang & Wensheng Yang, 2022. "Targeted reminders of electronic coupons: using predictive analytics to facilitate coupon marketing," Electronic Commerce Research, Springer, vol. 22(2), pages 321-350, June.
    3. Esmeli, Ramazan & Bader-El-Den, Mohamed & Abdullahi, Hassana, 2022. "An analyses of the effect of using contextual and loyalty features on early purchase prediction of shoppers in e-commerce domain," Journal of Business Research, Elsevier, vol. 147(C), pages 420-434.
    4. Nora Nahr & Marikka Heikkilä, 2022. "Uncovering the identity of Electronic Markets research through text mining techniques," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(3), pages 1257-1277, September.
    5. Xiaxia Ma & Wenliang Bian & Xiqing Yang & Shengnan Niu & Yongming Cai & Jie Guan & Wenbin Wang, 2022. "Online Retailer’s Contingent Free-Shipping Decisions under Large-Scale Promotions Considering Delayed Delivery," Sustainability, MDPI, vol. 14(17), pages 1-22, August.
    6. Wei Liu & Zongshui Wang & Hong Zhao, 2020. "Comparative study of customer relationship management research from East Asia, North America and Europe: A bibliometric overview," Electronic Markets, Springer;IIM University of St. Gallen, vol. 30(4), pages 735-757, December.
    7. Fatemeh Safara, 2022. "A Computational Model to Predict Consumer Behaviour During COVID-19 Pandemic," Computational Economics, Springer;Society for Computational Economics, vol. 59(4), pages 1525-1538, April.
    8. Ramazan Esmeli & Mohamed Bader-El-Den & Hassana Abdullahi, 2021. "Towards early purchase intention prediction in online session based retailing systems," Electronic Markets, Springer;IIM University of St. Gallen, vol. 31(3), pages 697-715, September.

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