Using Machine Learning and NLP for the Product Matching Problem-Reference-Cited by-同舟云学术

Using Machine Learning and NLP for the Product Matching Problem

Published:2023 Issue: Volume: Page:439-448
ISSN:2367-3370
Container-title:Intelligent Sustainable Systems
language:
Short-container-title:

Author:

de Santana Matheus Alcantara^ORCID,de Souza Baptista Cláudio^ORCID,Alves André Luiz Firmino^ORCID,Firmino Anderson Almeida^ORCID,da Silva Januário Gerson^ORCID,da Silva Caldera Roney Wellington^ORCID

Publisher

Springer Nature Singapore

Link

https://link.springer.com/content/pdf/10.1007/978-981-19-7663-6_41

Reference12 articles.

1. Barbosa, L.: Learning representations of web entities for entity resolution. Int. J. Web Info. Syst. 15(3), 346–358 (2019)

2. Barlaug, N., Gulla, J.A.: Neural networks for entity matching: a survey. ACM Trans. Knowl. Discov. Data 15(3) (2021)

3. Chapman, P., Clinton, J., Kerber, R., Khabaza, T., Reinartz, T., Shearer, C., Wirth, R.: Crisp-dm 1.0 step-by-step data mining guide. Technical report, The CRISP-DM consortium (2000)

4. Christen, P.: Febrl: an open source data cleaning, deduplication and record linkage system with a graphical user interface. In: Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. KDD’08, New York, NY, USA, Association for Computing Machinery, 1065–1068 (2008)

5. Christophides, V., Efthymiou, V., Herschel, M., Stefanidis, K.: Entity resolution in the web of data. In: Proceedings of the 23rd International Conference on World Wide Web. WWW’14 Companion, New York, NY, USA, Association for Computing Machinery, 203–204 (2014)

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Cross-Lingual Learning Strategies for Improving Product Matching Quality;Proceedings of the 39th ACM/SIGAPP Symposium on Applied Computing;2024-04-08