Smart Fashion Recommendation System using FashionNet

Abstract

An intelligent system known as a fashion suggestion system gives consumers personalised fashion advice based on their tastes, style, body shape, and other variables. The system analyses a user's data and predicts the best fashion products for them using data analytics, machine learning, and artificial intelligence approaches. Intelligent fashion suggestion is currently desperately needed due to the explosive expansion of fashion-focused trends. We create algorithms that automatically recommend users' attire based on their own fashion tastes. We investigate the use of deep networks to this difficult problem. Our technology, called FashionNet, is made up of two parts: a matching network for determining compatibility and a feature network for feature extraction. We create a two-stage training method that transfers a broad compatibility model to a model that embeds personal choice in order to achieve personalised recommendation.