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

Miguel Rocha (), Victor Høgheim (), Miguel Almeida (), Alex Theilmann (), José Silva (), Alessandro Gambetti (), Qiwei Han () and Maximilian Kaiser ()
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Miguel Rocha: Nova School of Business and Economics
Victor Høgheim: Nova School of Business and Economics
Miguel Almeida: Nova School of Business and Economics
Alex Theilmann: Nova School of Business and Economics
José Silva: Nova School of Business and Economics
Alessandro Gambetti: Nova School of Business and Economics
Qiwei Han: Nova School of Business and Economics
Maximilian Kaiser: Universitat Hamburg

A chapter in Advances in Digital Marketing and eCommerce, 2024, pp 127-134 from Springer

Abstract: Abstract 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.

Keywords: Product Matching; Deep Learning; E-Commerce; Precision-Recall Tradeoff (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-62135-2_13

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DOI: 10.1007/978-3-031-62135-2_13

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