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Fashion recommendation systems (FRS) have revolutionized e-commerce by offering tailored suggestions based on user preferences and behaviors. Some FRSs have traditionally incorporated gender as a filtering factor, often reinforcing binary classifications that may not fully accommodate diverse identities. This study examines both gender-aware and gender-blind approaches to fashion recommendation systems, investigating their implications for personalization, inclusivity, and the design of ethical AI. By analyzing insights from academic literature and industry applications, this review highlights advancements in Artificial Intelligence-driven recommendation techniques, proposing a research agenda to refine fashion AI systems that balance personalization and inclusivity. This work serves as a resource for researchers and industry practitioners to develop equitable fashion recommendation models.

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Gender-Aware or Gender-Blind Fashion Recommendation Systems: A State-of-the-Art Review and Future Agenda

  • 章节

  • 发表日期：

    2026年

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  • Robério Santos,
  • André Landim,
  • Diego F. B. Lima,
  • Renan de Araújo,
  • Antão Moura,
  • Evandro Costa,
  • Vanissa Wanick,
  • Eirini Bazaki

摘要

Fashion recommendation systems (FRS) have revolutionized e-commerce by offering tailored suggestions based on user preferences and behaviors. Some FRSs have traditionally incorporated gender as a filtering factor, often reinforcing binary classifications that may not fully accommodate diverse identities. This study examines both gender-aware and gender-blind approaches to fashion recommendation systems, investigating their implications for personalization, inclusivity, and the design of ethical AI. By analyzing insights from academic literature and industry applications, this review highlights advancements in Artificial Intelligence-driven recommendation techniques, proposing a research agenda to refine fashion AI systems that balance personalization and inclusivity. This work serves as a resource for researchers and industry practitioners to develop equitable fashion recommendation models.