Fashion Recommendation System using Deep Learning

For the purpose of forecast an individual's rating of a product or social entity, recommendation algorithms are essential. These techniques are applied an assortment of products, where personal preferences vary, such as movies, books, and restaurants. The two primary methods that areas frequently employed are collaborative and content-based filtering. The former takes into account the attributes of the items, whilst the later uses user behavior to provide suggestions. In this project, a fashion recommendation system based on image analysis and user preferences will be developed to offer customized outfit recommendations. The system processes and extracts characteristics from garment photos, including patterns, textures, and colours, by using Convolutional Neural Networks (CNNs). The algorithm customizes recommendations to suit individual preferences by incorporating user-specific data, such as skin tone, body shape, and past choices. The recommendation system improves user experience by suggesting appropriate things by combining content based approaches and collaborative filtering. Users may interact with the system, view recommendations, and make well-informed purchasing decisions thanks to an easy to-use interface.