Generative AI for Revolutionizing Personal Style

The fashion business is growing quickly and is one of the main forces behind the world's economies. The everexpanding business is driving a significant rise in the application of computer science to address various issues inside it. It becomes difficult to discover a personalized wardrobe that reflects their preferences, taste and needs. Traditional Fashion Recommendation Systems (FRSs) limits their ability to scale and adapt the ever growing styles as they heavily rely on manual design. Fashion can also be so inclusive, expressive and sustainable. Appropriate recommendations given by FRS helps to enhance user satisfaction and makes it more enjoyable and accessible. Artificial Intelligence (AI) tools have revolutionized FRS enabling them to consume beyond conventional methods by taking in contextual data, user preferences and visual content for recommendations with a more individualized suggestion. Recently, Generative Adversarial Networks (GANs) have emerged as a potent technique to enhance these systems by generating diverse fashion designs with high fidelity. In this paper, a systematic review of parameters used to evaluate FRS using Generative Algorithms is discussed. Various parameters to evaluate system performance and the recommendation quality are analyzed. Detailed analysis of the input parameters, to be considered to design the efficient AI based FRS (AI-FRS) is also presented. Along with this, research gaps are explored by surveying numerous review papers. This review will help in deciding the evaluation parameters to develop and examine more efficient AI based FRS.