Abstract
Artificial Intelligence (AI) has become a revolutionary force that has disrupted the fashion industry, bringing new-age solutions for design automation, personalized recommendation, and trend prediction.AI has transformed the fashion world by bringing innovation to design automation, personalized recommendation, and trend prediction. With the help of advanced machine learning techniques and extensive data analysis, ai is not only transforming the creative process but also revolutionizing the way consumers discover and interact with fashion. This research paper analyzes latest developments in AI-driven fashion systems, with an emphasis on customized styling agents, sketch-to-image generation, and real-time learning mechanisms. Based on eight leading research papers, it identifies key methodologies such as dataset generation (e.g., flora), generative models like gans and diffusion models, and adaptive components like kolmogorov-arnold networks (kan). The authors suggest a personalized AI fashion agent model, which integrates conversational interfaces, real-time trend analysis, and adaptive learning to offer users personalized fashion suggestions according to their preferences and the occasion. The work concludes by exploring the commercial feasibility of these systems, incorporating them into emerging technologies like augmented reality (AR) and virtual reality (VR), and directions for future research aimed at creating more ethical, inclusive, and user-focused fashion experiences.
Keywords
Artificial Intelligence, Fashion Recommendation, Personalized Styling, Deep Learning, FLORA Dataset, KAN.
Citations
IRE Journals:
Saladi Kalyan Somanna, Prabhjot Singh, Prerna Agarwal, Monisha H M "A Survey on AI-Driven Personalized Fashion Recommendation System" Iconic Research And Engineering Journals Volume 9 Issue 5 2025 Page 910-916 https://doi.org/10.64388/IREV9I5-1712003
IEEE:
Saladi Kalyan Somanna, Prabhjot Singh, Prerna Agarwal, Monisha H M
"A Survey on AI-Driven Personalized Fashion Recommendation System" Iconic Research And Engineering Journals, 9(5) https://doi.org/10.64388/IREV9I5-1712003
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