  Nowadays, recommender systems and search engines play an integral role in
fashion e-commerce. Still, many challenges lie ahead, and this study tries to
tackle some. This article first suggests a content-based fashion recommender
system that uses a parallel neural network to take a single fashion item shop
image as input and make in-shop recommendations by listing similar items
available in the store. Next, the same structure is enhanced to personalize the
results based on user preferences. This work then introduces a background
augmentation technique that makes the system more robust to out-of-domain
queries, enabling it to make street-to-shop recommendations using only a
training set of catalog shop images. Moreover, the last contribution of this
paper is a new evaluation metric for recommendation tasks called
objective-guided human score. This method is an entirely customizable framework
that produces interpretable, comparable scores from subjective evaluations of
human scorers.
