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

In: Advances in Digital Marketing and eCommerce

Author

Listed:
  • 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)

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.

Suggested Citation

  • Miguel Rocha & Victor Høgheim & Miguel Almeida & Alex Theilmann & José Silva & Alessandro Gambetti & Qiwei Han & Maximilian Kaiser, 2024. "Optimizing Product Matching in E-commerce: A Strategic Perspective on Precision-Recall Trade-Off," Springer Proceedings in Business and Economics, in: Francisco J. Martínez-López & Luis F. Martinez & Philipp Brüggemann (ed.), Advances in Digital Marketing and eCommerce, pages 127-134, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-62135-2_13
    DOI: 10.1007/978-3-031-62135-2_13
    as

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