Review of Personalized Outfit Recommender

In the age of looking good, we come across that one question everyday; What should I wear to look good today?. The question span a broad variety of topics, such as what am I missing in wardrobe? what is the current trend? will this suit my skin tone? will this work in current climate. the integration of technology and clothing has revolutionized the way individuals perceive and choose their outfits. This study presents a cutting-edge outfit recommendation system that combines the analysis of user skin tones, real-time weather data, and advanced machine learning algorithms. The system utilizes a digital wardrobe to store a vast array of fashion items and employs a Convolutional Neural Network (CNN) to extract intricate features from these items, enhancing the accuracy of clothing recognition. To personalize recommendations, the system incorporates user skin tone analysis, ensuring that the suggested outfits complement the wearer’s complexion. Additionally, real-time weather data is integrated to align outfit suggestions with the prevailing weather conditions, ensuring both style and comfort. The system utilizes K-Means clustering to categorize users based on similar fashion preferences and Decision Tree algorithms to refine outfit recommendations further. The proposed system not only enhances user experience but also contributes to sustainable fashion practices by promoting the optimal utilization of existing wardrobes, reducing impulsive shopping, and minimizing fashion waste. Through rigorous testing and validation, the system demonstrates its effectiveness in providing tailored outfit recommendations, thereby reshaping the future of personalized fashion choices