Science4Fashion: an end-to-end decision support system for fashion designers

Nowadays, the fashion clothing industry is moving towards “fast” fashion, offering a wide variety of products based on different patterns and styles, usually characterized by lower costs and ambiguous quality. The retails markets are trying to present regularly new fashion collections while trying to follow the latest fashion trends at the same time. The main reason is to remain competitive and keep up with ever-changing customer demands. Fashion designers draw inspiration from social media, e-shops, and fashion shows that set the new fashion trends. In this direction, we propose Science4Fashion, an AI end-to-end system that facilitates fashion designers by collecting and analyzing data from many different sources and suggesting products according to their needs. An overview of the system’s modules is presented, emphasizing data collection, data annotation using deep learning models, and product recommendation and user feedback processes. The experiments presented in this paper are twofold: (a) experiments regarding the evaluation of clothing attribute classification, and (b) experiments regarding product recommendation using the baseline kNN enriched by the frequency-based clustering algorithm (FBC), achieving promising results.