Fashion Recommendation Systems: From Single Items to Complete Outfits

Authors

  • Ilham KACHBAL LAPSSII, High School of Technology, Cadi Ayyad University, Safi, Morocco
  • Said EL ABDELLAOUI LAPSSII, High School of Technology, Cadi Ayyad University, Safi, Morocco
  • Khadija ARHID LAPSSII, High School of Technology, Cadi Ayyad University, Safi, Morocco

Keywords:

Diffusion Model, Deep Learning, Generative Adversarial Network, Recommendation Systems, Fashion Technology

Abstract

Fashion recommendation systems have evolved beyond traditional recommender systems to address the unique challenges of fashion retail and e-commerce. This paper presents a comprehensive categorization of these fashion recommendation systems, grouping them into four fundamental approaches: personalization-based, compatibility-based, context-based, and special applications. We examine how personalization-based approaches leverage user preferences, while compatibility-based methods address fashion coordination through visual and semantic matching. The paper also explores the progression from single-item recommendations to complete outfit generation, alongside the integration of contextual factors like climate and occasions. Additionally, special applications such as body-shape awareness and sustainable fashion demonstrate the expanding scope of the field. Through this categorization, the paper provides a structured framework for understanding current approaches and identifying promising directions for future research, offering valuable insights for both researchers and practitioners in fashion recommendation systems.

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Published

2025-04-27

How to Cite

[1]
I. KACHBAL, S. EL ABDELLAOUI, and K. ARHID, “Fashion Recommendation Systems: From Single Items to Complete Outfits”, IJCEDS, vol. 4, no. 1, pp. 27–40, Apr. 2025.

Issue

Vol. 4 No. 1 (2025): Volume 4. Issue 1

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Articles

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https://n2t.net/ark:/32155/IJCEDS.v4i1.79

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Copyright (c) 2025 Ilham KACHBAL, Said EL ABDELLAOUI, Khadija ARHID

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