Item Matching Model in E-Commerce: How Users Benefit

AliExpress (2023). Aliexpress. https://www.aliexpress.com/ Search in Google Scholar

Amazon (2023). Amazon. https://www.amazon.com/ref=nav_logo Search in Google Scholar

Akritidis, L., & Bozanis, P. (2018). Effective unsupervised matching of product titles with k-combinations and permutations. 2018 Innovations in Intelligent Systems and Applications (INISTA), 1-10. https://doi.org/10.1109/INISTA.2018.8466294 Search in Google Scholar

Baluch, A. (2023). 38 Ecommerce Statistics of 2023. https://www.forbes.com/advisor/business/ecommerce-statistics/ Search in Google Scholar

Cherednichenko, O., Ivashchenko, O., & Vovk, M. (2022). Towards pipeline construction for product matching task. Proceedings of the 41th International Conference on Organizational Science Development, 165-177. https://doi.org/10.18690/um.fov.3.2022.12 Search in Google Scholar

Cherednichenko, O., Vovk, M., Ivashchenko, O., Baggia, A., & Stratiienko, N. (2021). Improving item searching on trading platform based on reinforcement learning approach. COLINS. https://ceur-ws.org/Vol-2870/paper106.pdf Search in Google Scholar

Cherednichenko, O., Vovk, M., & Ivashchenko, O. (2020a). Item matching based on collection and processing customer perception of images. https://ceur-ws.org/Vol-2732/20200329.pdf Search in Google Scholar

Cherednichenko, O., Vovk, M., Kanishcheva, O., & Godlevskyi, M. (2018). Towards improving the search quality on the trading platforms. Information Systems: Research, Development, Applications, Education. SIGSAND/PLAIS 2018. Lecture Notes in Business Information Processing, 333. https://doi.org/10.1007/978-3-030-00060-8_2 Search in Google Scholar

Cherednichenko, O., Yanholenko, O., & Kanishcheva, O.V. (2020b). Developing the key attributes for product matching based on the item's image tag comparison. MoMLeT+DS. https://ceur-ws.org/Vol-2631/paper18.pdf Search in Google Scholar

Darú, G. H., Motta, F. D. da M., Castelo, A., & Loch, G. V. (2022). Short text classification applied to item description: Some methods evaluation. Semina: Ciências Exatas E Tecnológicas, 43(2), 189–198. https://doi.org/10.5433/1679-0375.2022v43n2p189 Search in Google Scholar

eBay (2023). eBay. https://www.ebay.com/ Search in Google Scholar

eCommerce in Europe (2023). Trend report. https://ecommercedb.com/reports/ecommerce-in-europe/589 Search in Google Scholar

Gopalakrishnan, V., Iyengar, S. P., Madaan, A., Rastogi, R., & Sengamedu, S. (2012). Matching product titles using web-based enrichment. Proceedings of the 21st ACM International Conference on Information and Knowledge Management, 605-614. https://doi.org/10.1145/2396761.2396839 Search in Google Scholar

Kannan, A., Givoni, I. E., Agrawal, R., & Fuxman, A. (2011). Matching unstructured product offers to structured product specifications. Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 404-412. https://doi.org/10.1145/2020408.2020474 Search in Google Scholar

Konda, P. V. (2018). Magellan: Toward building entity matching management systems. [Doctoral thesis, University of Wisconsin-Madison]. University of Wisconsin-Madison. Search in Google Scholar

Köpcke, H., Thor, A., & Rahm, E. (2010). Evaluation of entity resolution approaches on real-world match problems. Proceedings of the VLDB Endowment, 3, 484 - 493. https://doi.org/10.14778/1920841.1920904 Search in Google Scholar

Köpcke, H., Thor, A., Thomas, S., & Rahm, E. (2012). Tailoring entity resolution for matching product offers. Proceedings of the 15th International Conference on Extending Database Technology, 545-550. https://doi.org/10.1145/2247596.2247662 Search in Google Scholar

Krzeszewska, U., Poniszewska-Maranda, A., & Ochelska-Mierzejewska, J. (2022). Systematic comparison of vectorization methods in classification context. Applied Sciences, 12, 5119. https://doi.org/10.3390/app12105119 Search in Google Scholar

Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S., & Dean, J. (2013). Distributed representations of words and phrases and their compositionality. Advances in Neural Information Processing Systems, 26. https://papers.nips.cc/paper_files/paper/2013/file/9aa42b31882ec039965f3c4923ce901b-Paper.pdf Search in Google Scholar

Mudgal, S., Li, H., Rekatsinas, T., Doan, A., Park, Y., Krishnan, G., & Raghavendra, V. (2018). Deep learning for entity matching: A design space exploration. Proceedings of the 2018 International Conference on Management of Data, 19-34. https://doi.org/10.1145/3183713.3196926 Search in Google Scholar

Peeters, R., Bizer, C., & Glavas, G. (2020). Intermediate training of BERT for product matching. DI2KG. https://ceur-ws.org/Vol-2726/paper1.pdf Search in Google Scholar

Primpeli, A., Peeters, R., & Bizer, C. (2019). The WDC training dataset and gold standard for large-scale product matching. Companion Proceedings of the 2019 World Wide Web Conference, 381-386. https://doi.org/10.1145/3308560.3316609 Search in Google Scholar

Singh, A. K., & Shashi, M. (2019). Vectorisation of text documents for identifying unifiable news articles. International Journal of Advanced Computer Science and Applications, 10(7), 305–310. https://doi.org/10.14569/ijacsa.2019.0100742 Search in Google Scholar

Scikit-learn (2023). Scikit-learn. Machine learning in Python. https://scikit-learn.org Search in Google Scholar

SpaCy (2023). Industrial-strength natural language processing. https://spacy.io Search in Google Scholar

Wachsmuth, H. (2015). Text analysis pipelines. Towards ad-hoc large-scale text mining. Springer Cham. https://doi.org/10.1007/978-3-319-25741-9 Search in Google Scholar

Wachsmuth, H., Rose, M., & Engels, G. (2013). Automatic pipeline construction for real-time annotation. Computational Linguistics and Intelligent Text Processing. CICLing 2013. Lecture Notes in Computer Science, 7816. https://doi.org/10.1007/978-3-642-37247-6_4 Search in Google Scholar

Walmart (2023). Walmart. https://www.walmart.com/ Search in Google Scholar

Yang, X., Yang, K., Cui, T., Chen, M., & He, L. (2022). A study of text vectorization method combining topic model and transfer learning. Processes, 10(2), 350. https://doi.org/10.3390/pr10020350 Search in Google Scholar

Yang, Y., Liu, Y., Lu, X., Xu, J., & Wang, F. (2020). A named entity topic model for news popularity prediction. Knowledge-Based Systems, 208, 106430. https://doi.org/10.1016/j.knosys.2020.106430 Search in Google Scholar