Skip to main content
SpringerLink
Log in

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

  • Published:
Electronic Commerce Research Aims and scope Submit manuscript

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Notes

  1. Reported by Director of China Customs, http://data.mofcom.gov.cn/article/zxtj/201802/39837.html.

References

  1. Ding, L., Fan, Z., & Chen, D. (2015). Auto-categorization of HS code using background net approach. Procedia Computer Science,60, 1462–1471.

    Google Scholar 

  2. Li, G., Zheng, H., Ji, X., & Li, H. (2018). Game theoretical analysis of firms’ operational low-carbon strategy under various cap-and-trade mechanisms. Journal of Cleaner Production, 197(1), 124–133.

    Google Scholar 

  3. Sun, J., Li, G., & Wang, Z. (2018). Optimizing China’s energy consumption structure under energy and carbon constraints. Structural Change and Economic Dynamics, 47, 57–72.

    Google Scholar 

  4. Sun, J., Wang, Z., & Li, G. (2018). Measuring emission-reduction and energy-conservation efficiency of Chinese cities considering management and technology heterogeneity. Journal of Cleaner Production, 175, 561–571.

    Google Scholar 

  5. Macgabhann, L. (2012). The e-commerce sales and use tax controversy: Is the end near? Journal of Information Systems Management,29(4), 331–337.

    Google Scholar 

  6. Dakhlia, S., & Strauss, R. P. (2013). Should sales taxes be imposed on e-commerce? Journal of Public Economic Theory,15(6), 803–828.

    Google Scholar 

  7. Hughes, J. F., & Glaister, K. (2001). Electronic commerce and international taxation: A square pegin a round hole? European Management Journal,19(6), 651–658.

    Google Scholar 

  8. Grüschow, R. M., Kemper, J., & Brettel, M. (2016). How do different payment methods deliver cost and credit efficiency in electronic commerce? Electronic Commerce Research and Applications,18, 27–36.

    Google Scholar 

  9. Martin, A. K., & Andrade, N. N. G. D. (2013). Friending the taxman: On the use of social networking services for government aid in Europe. Telecommunications Policy,37(37), 715–724.

    Google Scholar 

  10. Yapar, B. K., Bayrakdar, S., & Yapar, M. (2015). The role of taxation problems on the development of e-commerce. Procedia-Social and Behavioral Sciences,195(2), 642–648.

    Google Scholar 

  11. Wu, Y., Lau, T., Atkin, D. J., & Lin, C. A. (2011). A comparative study of online privacy regulations in the U.S. and China. Telecommunications Policy,35(7), 603–616.

    Google Scholar 

  12. Han, W. (2018). The analysis on Chinese e-commerce tax losses based on the perspective of information asymmetry. Electronic Commerce Research. https://doi.org/10.1007/s10660-018-9318-7.

    Article  Google Scholar 

  13. Li, G., Zheng, H., Sethi, S. P., Guan, X. (2018). Inducing Downstream Information Sharing via Manufacturer Information Acquisition and Retailer Subsidy. Decision Sciences. https://doi.org/10.1111/deci.12340.

  14. Hsu, V. N., & Zhu, K. (2011). Tax-effective supply chain decisions under China’s export-oriented tax policies. Manufacturing & Service Operations Management,13(2), 163–179.

    Google Scholar 

  15. Raus, M., Flügge, B., & Boutellier, R. (2009). Electronic customs innovation: An improvement of governmental infrastructures. Government Information Quarterly,26(2), 246–256.

    Google Scholar 

  16. Urciuoli, L., Hintsa, J., & Ahokas, J. (2013). Drivers and barriers affecting usage of e-Customs—A global survey with customs administrations using multivariate analysis techniques. Government Information Quarterly,30(4), 473–485.

    Google Scholar 

  17. Appeals, T., & Swielande, H. S. D. (1998). Rolling back the frontiers: The customs clearance revolution. International Journal of Logistics Management,9(1), 111–118.

    Google Scholar 

  18. Hsu, C. I., Shih, H. H., & Wang, W. C. (2009). Applying RFID to reduce delay in import cargo customs clearance process. Computers & Industrial Engineering,57(2), 506–519.

    Google Scholar 

  19. Pourakbar, M., & Zuidwijk, R. A. (2018). The role of customs in securing containerized global supply chains. European Journal of Operational Research,271(1), 331–340.

    Google Scholar 

  20. Rovetta, D. (2009). Some reflections on customs classification and the harmonized system as tools for interpreting the schedules of commitments under GATT article II. Legal Issues of Economic Integration,36, 7–22.

    Google Scholar 

  21. Abdolshah, M., Teimouri, M., & Rahmani, R. (2017). Classification of X-ray images of shipping containers. Expert Systems with Applications,77, 57–65.

    Google Scholar 

  22. Lee, J., Lee, H. S., & Wang, P. (2004). An interactive visual interface for online product catalogs. Electronic Commerce Research,4(4), 335–358.

    Google Scholar 

  23. Aanen, S. S., Vandic, D., & Frasincar, F. (2015). Automated product taxonomy mapping in an e-commerce environment. Expert Systems with Applications,42(3), 1298–1313.

    Google Scholar 

  24. Lee, H., & Yoon, Y. (2018). Engineering doc2vec for automatic classification of product descriptions on O2O applications. Electronic Commerce Research,18(3), 433–456.

    Google Scholar 

  25. Bai, X., Shi, B., Zhang, C., Cai, X., & Qi, L. (2017). Text/non-text image classification in the wild with convolutional neural networks. Pattern Recognition,66, 437–446.

    Google Scholar 

  26. Han, D., Liu, Q., & Fan, W. (2018). A new image classification method using CNN transfer learning and web data augmentation. Expert Systems with Applications,95, 43–56.

    Google Scholar 

  27. Kazemian, H. B., & Ahmed, S. (2015). Comparisons of machine learning techniques for detecting malicious webpages. Expert Systems with Applications,42(3), 1166–1177.

    Google Scholar 

  28. Ramesh, G., Gupta, J., & Gamya, P. G. (2017). Identification of phishing webpages and its target domains by analyzing the feign relationship. Journal of Information Security and Applications,35, 75–84.

    Google Scholar 

  29. Kim, S., Kim, J., Nam, S., & Kim, D. (2018). WebMon: ML-and YARA-based malicious webpage detection. Computer Networks,137, 119–131.

    Google Scholar 

  30. Abbasi, A., Zhang, Z., Zimbra, D., Chen, H., & Nunamaker, J. F., Jr. (2010). Detecting fake Websites: The contribution of statistical learning theory. MIS Quarterly,3, 435–461.

    Google Scholar 

  31. Song, L., Lau, R. Y. K., Kwok, R. C. W., Mirkovski, K., & Dou, W. (2017). Who are the spoilers in social media marketing? Incremental learning of latent semantics for social spam detection. Electronic Commerce Research,17(1), 51–81.

    Google Scholar 

  32. Huang, A. (2018). A risk detection system of e-commerce: Researches based on soft information extracted by affective computing web texts. Electronic Commerce Research,18(1), 143–157.

    Google Scholar 

  33. Xiao, B., & Benbasat, I. (2011). Product-related deception in e-commerce: A theoretical perspective. MIS Quarterly,35(1), 169–196.

    Google Scholar 

  34. Zhang, X., Zhang, X., Li, X., Li, Z., & Wang, S. (2018). Classify social image by integrating multi-modal content. Multimedia Tools and Applications,77(6), 7469–7485.

    Google Scholar 

  35. Kalva, P., Enembreck, F., & Koerich, A. (2007, September). Web image classification based on the fusion of image and text classifiers. In Ninth international conference on document analysis and recognition, 2007. ICDAR 2007 (Vol. 1, pp. 561–568). IEEE.

  36. Zhu, F., Ma, Z., Li, X., Chen, G., Chien, J. T., Xue, J. H., et al. (2018). Image-text dual neural network with decision strategy for small-sample image classification. Neurocomputing. https://doi.org/10.1016/j.neucom.2018.02.099.

    Article  Google Scholar 

  37. Cheng, F., Wang, S. L., & Liew, A. W. C. (2018). Visual speaker authentication with random prompt texts by a dual-task CNN framework. Pattern Recognition,83, 340–352.

    Google Scholar 

  38. Wu, H., Huang, Q., Wang, D., & Gao, L. (2018). A CNN-SVM combined model for pattern recognition of knee motion using mechanomyography signals. Journal of Electromyography and Kinesiology,42, 136–142.

    Google Scholar 

  39. Swapna, G., Kp, S., & Vinayakumar, R. (2018). Automated detection of diabetes using CNN and CNN-LSTM network and heart rate signals. Procedia Computer Science,132, 1253–1262.

    Google Scholar 

  40. Xiao, B., & Benbasat, I. (2007). E-commerce product recommendation agents: Use, characteristics, and impact. MIS Quarterly,31(1), 137–209.

    Google Scholar 

  41. Robinson, R., Goh, T. T., & Zhang, R. (2012). Textual factors in online product reviews: A foundation for a more influential approach to opinion mining. Electronic Commerce Research,12(3), 301–330.

    Google Scholar 

  42. He, L., Zhang, N., & Yin, L. (2018). The evaluation for perceived quality of products based on text mining and fuzzy comprehensive evaluation. Electronic Commerce Research,18(2), 277–289.

    Google Scholar 

  43. Jing, N., Jiang, T., Du, J., & Sugumaran, V. (2018). Personalized recommendation based on customer preference mining and sentiment assessment from a Chinese e-commerce website. Electronic Commerce Research,18(1), 159–179.

    Google Scholar 

  44. Ma, Y., Chen, G., & Wei, Q. (2017). Finding users preferences from large-scale online reviews for personalized recommendation. Electronic Commerce Research,17(1), 3–29.

    Google Scholar 

  45. Tanha, J., van Someren, M., & Afsarmanesh, H. (2017). Semi-supervised self-training for decision tree classifiers. International Journal of Machine Learning and Cybernetics,8(1), 355–370.

    Google Scholar 

  46. Farid, D. M., Zhang, L., Rahman, C. M., Hossain, M. A., & Strachan, R. (2014). Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks. Expert Systems with Applications,41(4), 1937–1946.

    Google Scholar 

  47. Schu, G., & Scharcanski, J. (2017). A new method for building adaptive Bayesian trees and its application in color image segmentation. Expert Systems with Applications,98, 57–71.

    Google Scholar 

  48. Bi, W., Cai, M., Liu, M., & Li, G. (2016). A big data clustering algorithm for mitigating the risk of customer churn. IEEE Transactions on Industrial Informatics, 12(3), 1270–1281.

    Google Scholar 

  49. Fukushima, K. (1980). Neocognitron A self-organizing neural network model for a mechanism of pattern recognition unaffected by shift position. Biological Cybernetics,36, 193–202.

    Google Scholar 

  50. LeCun, Y., Boser, B., Denker, J. S., Henderson, D., Howard, R. E., Hubbard, W., et al. (1989). Backpropagation applied to handwritten zip code recognition. Neural Computation,1(4), 541–551.

    Google Scholar 

  51. Wei, Y., Xia, W., Lin, M., Huang, J., Ni, B., Dong, J., et al. (2016). Hcp: A flexible cnn framework for multi-label image classification. IEEE Transactions on Pattern Analysis and Machine Intelligence,38(9), 1901–1907.

    Google Scholar 

Download references

Acknowledgements

The authors sincerely thank the editors and anonymous reviewers for their insightful comments and suggestions. This research is partially supported by the National Natural Science Foundation of China under the grant nos. 91746110, 71372019, 71521002, 71642004; the Joint Development Program of Beijing Municipal Commission of Education.

Author information

Authors and Affiliations

  1. School of Management and Economics, Beijing Institute of Technology, Beijing, 100081, China

    Guo Li & Na Li

  2. Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100081, China

    Guo Li & Na Li

  3. Sustainable Development Research Institute for Economy and Society of Beijing, Beijing, 100081, China

    Guo Li

Authors
  1. Guo Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Na Li
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Guo Li.

Ethics declarations

Conflict of interest

On behalf of all authors, the corresponding author states that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10660-019-09334-x

Keywords

Search

Navigation