Fashion Recommendation Systems: From Single Items to Complete Outfits

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.