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Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network

Author

Listed:
  • Guo Li

    (Beijing Institute of Technology
    Beijing Institute of Technology
    Sustainable Development Research Institute for Economy and Society of Beijing)

  • Na Li

    (Beijing Institute of Technology
    Beijing Institute of Technology)

Abstract

Customs classification is an essential international procedure to import cross-border goods traded by various companies and individuals. Proper classification of such goods with high efficiency in light of the rapidly increasing amount of international trade is still challenging. The current abundant e-commence data and advanced machine learning techniques provide an opportunity for cross-border e-commerce sellers to classify goods efficiently. Thus, in this paper, we propose a text-image adaptive convolutional neural network to effectively utilize website information and facilitate the customs classification process. The proposed model includes two independent submodels: one for text and the other for image. The submodels are fused by a novel method, which can adjust the value of parameters according to the model training result. Finally, we conduct a case study and comparison experiments based on a group of customs tariff codes and a data set from an e-commerce website. Experiment results indicate the effectiveness of text and image combination in performance improvement, the outperformance of the adaptive fusion method, as well as the potential of this approach when applied to customs classification.

Suggested Citation

  • Guo Li & Na Li, 2019. "Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network," Electronic Commerce Research, Springer, vol. 19(4), pages 779-800, December.
  • Handle: RePEc:spr:elcore:v:19:y:2019:i:4:d:10.1007_s10660-019-09334-x
    DOI: 10.1007/s10660-019-09334-x
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    Cited by:

    1. Chuanmin Mi & Yijing Wang & Lin Xiao, 2021. "Prediction on transaction amounts of China’s CBEC with improved GM (1, 1) models based on the principle of new information priority," Electronic Commerce Research, Springer, vol. 21(1), pages 125-146, March.
    2. Wei-Lun Huang & Peng Hu & Sophia Tsai & Xi-Ding Chen, 2021. "The business analysis on the home-bias of E-commerce consumer behavior," Electronic Commerce Research, Springer, vol. 21(3), pages 855-879, September.
    3. Lina Guo, 2022. "Cross-border e-commerce platform for commodity automatic pricing model based on deep learning," Electronic Commerce Research, Springer, vol. 22(1), pages 1-20, March.
    4. Olha Borysenko & Olena Vasyl’yeva & Olga Katerna & Iuliia Masiuk & Oleg Panakhi, 2022. "Modeling the Risks of the Global Customs Space," JRFM, MDPI, vol. 15(12), pages 1-23, December.

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