  Using embeddings as representations of products is quite commonplace in
recommender systems, either by extracting the semantic embeddings of text
descriptions, user sessions, collaborative relationships, or product images. In
this paper, we present an approach to extract style embeddings for using in
fashion recommender systems, with a special focus on style information such as
textures, prints, material, etc. The main issue of using such a type of
embeddings is its high dimensionality. So, we propose feature reduction
solutions alongside the investigation of its influence in the overall task of
recommending products of the same style based on their main image. The feature
reduction we propose allows for reducing the embedding vector from 600k
features to 512, leading to a memory reduction of 99.91\% without critically
compromising the quality of the recommendations.
