Machine Learning for Personalized Fashion Recommendation Systems: A Review

The rapid digital transformation of the fashion industry has amplified the need for intelligent tools that can

navigate vast product catalogs and deliver personalized recommendations to consumers. Fashion recommender systems,

leveraging machine learning and deep learning techniques, have emerged as a crucial solution to address challenges such as

choice overload, style compatibility, and changing trends. This review examines the evolution of fashion recommender

systems, from early content-based and collaborative filtering approaches to hybrid, context-aware, and deep learning-based

models capable of processing multimodal data. The methodology involved a structured literature search across major

academic databases, focusing on recent studies that directly address clothing and fashion recommendation. Key findings

reveal that while significant progress has been made in personalization accuracy, diversity, and visual understanding,

persistent limitations remain. These include reliance on small or proprietary datasets, lack of demographic and cultural

diversity, inconsistent evaluation protocols, and domain shifts between curated catalog images and real-world contexts.

Addressing these challenges will require the development of standardized, diverse benchmark datasets, transparent

experimental reporting, and the integration of ethical considerations such as fairness, inclusivity, and privacy. This paper

provides a comprehensive synthesis of existing research, identifies current gaps, and outlines future directions for building

robust, contextually aware, and user-centered fashion recommender systems.