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The evolution of e-commerce platforms can be characterized by the low digital maturity level of represented stores, their limited data amounts and the fragmentation of existent data. In order to overcome these challenges, we present an e-commerce extension library that collects customer sales predictions and clusterization functionalities grouped into two distinct modules: the Federated Learning and personalized module. As a proof of concept, a prototype is presented in this paper that has been developed in the post-Covid context to boost the digitalization capability of small short commerce.

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Privacy-Preserving and Personalized AI Modules for E-Commerce Platforms

  • 会议论文

  • 发表日期：

    2025年

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  • Arantzazu Florez Tapia,
  • Izar Azpiroz,
  • Ane M. Florez-Tapia,
  • Amaia Gil,
  • Elena Zotova,
  • Roger Solsona,
  • Igor García Olaizola,
  • Marco Quartulli

摘要

The evolution of e-commerce platforms can be characterized by the low digital maturity level of represented stores, their limited data amounts and the fragmentation of existent data. In order to overcome these challenges, we present an e-commerce extension library that collects customer sales predictions and clusterization functionalities grouped into two distinct modules: the Federated Learning and personalized module. As a proof of concept, a prototype is presented in this paper that has been developed in the post-Covid context to boost the digitalization capability of small short commerce.