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

Guo Li*, Na Li

*Corresponding author for this work

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    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.

    Original languageEnglish
    Pages (from-to)779-800
    Number of pages22
    JournalElectronic Commerce Research
    Volume19
    Issue number4
    DOIs
    Publication statusPublished - 1 Dec 2019

    Keywords

    • Convolutional neural network
    • Cross-border e-commerce
    • Customs classification
    • Text and image classification

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    Li, G., & Li, N. (2019). Customs classification for cross-border e-commerce based on text-image adaptive convolutional neural network. Electronic Commerce Research, 19(4), 779-800. https://doi.org/10.1007/s10660-019-09334-x