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An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior

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  • Weiwei Zhang
  • Mingyan Wang

Abstract

As the Internet retail industry continues to rise, more and more consumers choose to shop online, especially Chinese consumers. Using consumer behavior data left on the Internet to predict repurchase behavior is of great significance for companies to achieve precision marketing. This paper proposes an improved deep forest model, and the interactive behavior characteristics of users and goods are added into the original feature model to predict the repurchase behavior of e-commerce consumers. Based on the Alibaba mobile e-commerce platform data set, first construct a feature engineering that includes user characteristics, product characteristics, and interactive behavior characteristics. And then use our proposed model to make predictions. Experiments show that the model’s overall performance with increased interactive behavior features is better and has higher accuracy. Compared with the existing prediction models, the improved deep forest model has certain advantages, which not only improves the prediction accuracy but also reduces the cost of training time.

Suggested Citation

  • Weiwei Zhang & Mingyan Wang, 2021. "An improved deep forest model for prediction of e-commerce consumers’ repurchase behavior," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-16, September.
    • Handle: RePEc:plo:pone00:0255906
    DOI: 10.1371/journal.pone.0255906
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    References listed on IDEAS

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    Cited by:

    1. Cen, Xiao & Chen, Zengliang & Chen, Haifeng & Ding, Chen & Ding, Bo & Li, Fei & Lou, Fangwei & Zhu, Zhenyu & Zhang, Hongyu & Hong, Bingyuan, 2024. "User repurchase behavior prediction for integrated energy supply stations based on the user profiling method," Energy, Elsevier, vol. 286(C).
    2. Qing Yang & Naeem Hayat & Abdullah Al Mamun & Zafir Khan Mohamed Makhbul & Noor Raihani Zainol, 2022. "Sustainable customer retention through social media marketing activities using hybrid SEM-neural network approach," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-23, March.

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