Optimizing Product Matching in E-commerce: A Strategic Perspective on Precision-Recall Trade-Off
摘要
This paper explores product matching and its vital role in e-commerce to match products across different platforms. We propose a multi-stage, deep learning-based system and evaluate its performance based on the precision-recall trade-off. Our research aimed to determine the most effective strategies for e-commerce platforms to accurately align product listings, a critical operation for maintaining competitive marketplaces.