  We develop a two-stage deep learning framework that recommends fashion images
based on other input images of similar style. For that purpose, a neural
network classifier is used as a data-driven, visually-aware feature extractor.
The latter then serves as input for similarity-based recommendations using a
ranking algorithm. Our approach is tested on the publicly available Fashion
dataset. Initialization strategies using transfer learning from larger product
databases are presented. Combined with more traditional content-based
recommendation systems, our framework can help to increase robustness and
performance, for example, by better matching a particular customer style.
