  Building effective recommender systems for domains like fashion is
challenging due to the high level of subjectivity and the semantic complexity
of the features involved (i.e., fashion styles). Recent work has shown that
approaches to `visual' recommendation (e.g.~clothing, art, etc.) can be made
more accurate by incorporating visual signals directly into the recommendation
objective, using `off-the-shelf' feature representations derived from deep
networks. Here, we seek to extend this contribution by showing that
recommendation performance can be significantly improved by learning `fashion
aware' image representations directly, i.e., by training the image
representation (from the pixel level) and the recommender system jointly; this
contribution is related to recent work using Siamese CNNs, though we are able
to show improvements over state-of-the-art recommendation techniques such as
BPR and variants that make use of pre-trained visual features. Furthermore, we
show that our model can be used \emph{generatively}, i.e., given a user and a
product category, we can generate new images (i.e., clothing items) that are
most consistent with their personal taste. This represents a first step towards
building systems that go beyond recommending existing items from a product
corpus, but which can be used to suggest styles and aid the design of new
products.
