Optimizing Product Matching in E-commerce: A Strategic Perspective on Precision-Recall Trade-Off

Author:

Rocha Miguel,Høgheim Victor,Almeida Miguel,Theilmann Alex,Silva José,Gambetti Alessandro,Han Qiwei,Kaiser Maximilian

Publisher

Springer Nature Switzerland

Reference15 articles.

1. Barlaug, N., Gulla, J.A.: Neural networks for entity matching: a survey. ACM Trans. Knowl. Discov. Data 15(3), 52 (2021)

2. Bilenko, M., Kamath, B., Mooney,  R.J.: Adaptive blocking: learning to scale up record linkage. In: Sixth International Conference on Data Mining (ICDM 2006), pp. 87–96 (2006)

3. Brunner, U., Stockinger, K.: Entity matching with transformer architectures - a step forward in data integration. In: Proceedings of EDBT 2020, pp. 463–473 (2020)

4. Bojanowski, P., Grave, E., Joulin, A., Mikolov, T.: Enriching word vectors with subword information. Trans. Assoc. Comput. Linguist. 5(June), 135–146 (2017)

5. Bungee Tech: ML Product Matching: A Key Factor to Retail Success (2023).  https://bungeetech.com/Product-Matching-Key-to-Success/

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