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, Maximilian Kaiser

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationAdvances in Digital Marketing and eCommerce - 5th International Conference, 2024
EditorsFrancisco J. Martínez-López, Luis F. Martinez, Philipp Brüggemann
PublisherSpringer Nature
Pages127-134
Number of pages8
ISBN (Print)9783031621345
DOIs
Publication statusPublished - 2024
Event5th International Conference on Digital Marketing and eCommerce Conference, DMEC 2024 - Barcelona, Spain
Duration: 26 Jun 202428 Jun 2024

Publication series

NameSpringer Proceedings in Business and Economics
ISSN (Print)2198-7246
ISSN (Electronic)2198-7254

Conference

Conference5th International Conference on Digital Marketing and eCommerce Conference, DMEC 2024
Country/TerritorySpain
CityBarcelona
Period26/06/2428/06/24

Keywords

  • Deep Learning
  • E-Commerce
  • Precision-Recall Tradeoff
  • Product Matching

Cite this