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Deep cross-platform product matching in e-commerce

  • Juan Li
  • Zhicheng DouEmail author
  • Yutao Zhu
  • Xiaochen Zuo
  • Ji-Rong Wen
eCommerce Search and Recommendation
  • 9 Downloads

Abstract

Online shopping has become more and more popular in recent years, which leads to a prosperity on online platforms. Generally, the identical products are provided by many sellers on multiple platforms. Thus the comparison between products on multiple platforms becomes a basic demand for both consumers and sellers. However, identifying identical products on multiple platforms is difficult because the description for a certain product can be various. In this work, we propose a novel neural matching model to solve this problem. Two kinds of descriptions (i.e. product titles and attributes), which are widely provided on online platforms, are considered in our method. We conduct experiments on a real-world data set which contains thousands of products on two online e-commerce platforms. The experimental results show that our method can take use of the product information contained in both titles and attributes and significantly outperform the state-of-the-art matching models.

Keywords

Product matching Neural network Text matching 

Notes

Acknowledgements

This work was supported by National Key R&D Program of China No. 2018YFC0830703, National Natural Science Foundation of China No. 61872370, and the Fundamental Research Funds for the Central Universities, and the Research Funds of Renmin University of China No. 2112018391.

References

  1. Christen, P. (2012). Data matching: Concepts and techniques for record linkage, entity resolution, and duplicate detection. Data-Centric Systems and Applications, Springer 2012 (pp. 1–270).Google Scholar
  2. Duan, H., & Zhai, C. X. (2015). Mining coordinated intent representation for entity search and recommendation. In Proceedings of the 24th ACM international conference on information and knowledge management, CIKM 2015, Melbourne, VIC, Australia, October 19–23, 2015 (pp. 333–342).Google Scholar
  3. Duan, H., Zhai, C. X., Cheng, J., & Gattani, A. (2013). Supporting keyword search in product database: A probabilistic approach. PVLDB, 6(14), 1786–1797.Google Scholar
  4. Fellegi, I. P., & Sunter, A. B. (1969). A theory for record linkage. Publications of the American Statistical Association, 64(328), 1183–1210.CrossRefzbMATHGoogle Scholar
  5. Gopalakrishnan, V., Sengamedu, S., Sengamedu, S., Sengamedu, S., & Sengamedu, S. (2012). Matching product titles using web-based enrichment. In ACM international conference on information and knowledge management (pp. 605–614).Google Scholar
  6. Guo, J., Fan, Y., Ai, Q., & Croft, W. B. (2016). A deep relevance matching model for ad-hoc retrieval. In Proceedings of the 25th ACM international conference on information and knowledge management, CIKM 2016, Indianapolis, IN, USA, October 24–28, 2016 (pp. 55–64).Google Scholar
  7. Hu, B., Lu, Z., Li, H., & Chen, Q. (2014). Convolutional neural network architectures for matching natural language sentences. In Advances in neural information processing systems 27: Annual conference on neural information processing systems 2014, December 8–13 2014, Montreal, Quebec, Canada (pp. 2042–2050).Google Scholar
  8. Huang, P.-S., He, X., Gao, J., Deng, L., Acero, A., & Heck, L. P. (2013). Learning deep structured semantic models for web search using clickthrough data. In 22nd ACM international conference on information and knowledge management, CIKM’13, San Francisco, CA, USA, October 27–November 1, 2013 (pp. 2333–2338).Google Scholar
  9. Kannan, Anitha, Givoni, I. E., Agrawal, R., & Fuxman, A. (2011). Matching unstructured product offers to structured product specifications. In Proceedings of the 17th ACM SIGKDD international conference on knowledge discovery and data mining, San Diego, CA, USA, August 21–24, 2011 (pp. 404–412).Google Scholar
  10. Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. CoRR arXiv:1412.6980.
  11. Köpcke, H., Thor, A., Thomas, S., & Rahm, E. (2012). Tailoring entity resolution for matching product offers. In 15th international conference on extending database technology, EDBT ’12, Berlin, Germany, March 27–30, 2012, proceedings (pp. 545–550).Google Scholar
  12. Mikolov, T., Chen, K., Corrado, G., & Dean, J. (2013a). Efficient estimation of word representations in vector space. CoRR, arXiv:1301.3781.
  13. Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013b). Distributed representations of words and phrases and their compositionality. In Advances in neural information processing systems 26: 27th annual conference on neural information processing systems 2013. Proceedings of a meeting held December 5–8, 2013, Lake Tahoe, Nevada, United States (pp. 3111–3119).Google Scholar
  14. Mitra, B., & Craswell, N. (2018). An introduction to neural information retrieval. Foundations and Trends in Information Retrieval, 13(1), 1–126.CrossRefGoogle Scholar
  15. Mitra, B., Diaz, F., & Craswell, N. (2017). Learning to match using local and distributed representations of text for web search. In Proceedings of the 26th international conference on World Wide Web, WWW 2017, Perth, Australia, April 3–7, 2017 (pp. 1291–1299).Google Scholar
  16. Nauman, F., & Herschel, M. (2010). An introduction to duplicate detection. Synthesis Lectures on Data Management, 2(1), 1–87.CrossRefGoogle Scholar
  17. Onal, K. D., Zhang, Y., Altingovde, I. S., Rahman, Md., Mustafizur, K., Pinar, B., et al. (2018). Neural information retrieval: At the end of the early years. Information Retrieval Journal, 21(2–3), 111–182.CrossRefGoogle Scholar
  18. Pang, L., Lan, Y., Guo, J., Xu, J., Wan, S., & Cheng, X. (2016). Text matching as image recognition. In Proceedings of the thirtieth AAAI conference on artificial intelligence, February 12–17, 2016, Phoenix, Arizona, USA (pp. 2793–2799).Google Scholar
  19. Qiu, X., & Huang, X. (2015). Convolutional neural tensor network architecture for community-based question answering. In Proceedings of the twenty-fourth international joint conference on artificial intelligence, IJCAI 2015, Buenos Aires, Argentina, July 25–31, 2015 (pp. 1305–1311).Google Scholar
  20. Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE Transaction on Signal Processing, 45(11), 2673–2681.CrossRefGoogle Scholar
  21. Shen, Y., He, X., Gao, J., Deng, L., & Mesnil, G. (2014). Learning semantic representations using convolutional neural networks for web search. In 23rd international World Wide Web conference, WWW ’14, Seoul, Republic of Korea, April 7–11, 2014, Companion Volume (pp. 373–374).Google Scholar
  22. Van Gysel, C., de Rijke, M., & Kanoulas, E. (2016). Learning latent vector spaces for product search. In Proceedings of the 25th ACM international conference on information and knowledge management, CIKM 2016, Indianapolis, IN, USA, October 24–28, 2016 (pp. 165–174).Google Scholar
  23. Wan, S., Lan, Y., Guo, J., Xu, J., Pang, L., & Cheng, X. (2016). A deep architecture for semantic matching with multiple positional sentence representations. In Proceedings of the thirtieth AAAI conference on artificial intelligence, February 12–17, 2016, Phoenix, Arizona, USA (pp. 2835–2841).Google Scholar
  24. Winkler, W. (2006). Overview of record linkage and current research directions (pp. 603–623). Suitland: Bureau of the Census.Google Scholar
  25. Winkler, W. E. (1999). The state of record linkage and current research problems. Suitland: Statistical Research Division, U.S. Census Bureau.Google Scholar
  26. Xiong, C., Dai, Z., Callan, J., Liu, Z., & Power, R. (2017). End-to-end neural ad-hoc ranking with kernel pooling. In Proceedings of the 40th international ACM SIGIR conference on research and development in information retrieval, Shinjuku, Tokyo, Japan, August 7–11, 2017 (pp. 55–64).Google Scholar
  27. Yan, R., Wu, H., Wu, H., & Wu, H. (2016). “Shall I be your chat companion?”: Towards an online human–computer conversation system. In ACM international on conference on information and knowledge management (pp. 649–658).Google Scholar
  28. Zamani, H., Mitra, B., Song, X., Craswell, N., & Tiwary, S. (2018). Neural ranking models with multiple document fields. In Proceedings of the Eleventh ACM international conference on web search and data mining, WSDM 2018, Marina Del Rey, CA, USA, February 5–9, 2018 (pp. 700–708).Google Scholar
  29. Zhang, B., Su, J., Xiong, D., Lu, Y., Duan, H., & Yao, J. (2015). Shallow convolutional neural network for implicit discourse relation recognition. In Conference on empirical methods in natural language processing (pp. 2230–2235).Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of InformationRenmin University of ChinaBeijingPeople’s Republic of China
  2. 2.Beijing Key Laboratory of Big Data Management and Analysis MethodsBeijingChina

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