Outfit Completion and Clothes Recommendation

Recommending fashion outfits requires learning a concept of style and fashionability that is typically human. There has been an increasing research effort into creating Machine Learning models able to learn such concepts, in order to distinguish between compatible and incompatible clothes and to select an item that would complete an outfit. However, most of the work done in literature tackles this problem from a pure Machine Learning point of view, disregarding real-case scenarios and the human interaction with systems able to generate outfits. This work tries to move the problem of generating outfits to the Recommender Systems domain by presenting as its main contribution a novel algorithm for a fashion-specific Recommender System that generates fashionable outfits, able to scale its inference time to be useful in real use case scenarios, and applies such algorithm on public and industrial datasets. In addition to this, this work shows preliminary results on how this algorithm can be employed in a real scenario and reports as preliminary results the evaluations provided by three professional stylists on the outfits generated by such algorithms.