  Outfits in online fashion data are composed of items of many different types
(e.g. top, bottom, shoes) that share some stylistic relationship with one
another. A representation for building outfits requires a method that can learn
both notions of similarity (for example, when two tops are interchangeable) and
compatibility (items of possibly different type that can go together in an
outfit). This paper presents an approach to learning an image embedding that
respects item type, and jointly learns notions of item similarity and
compatibility in an end-to-end model. To evaluate the learned representation,
we crawled 68,306 outfits created by users on the Polyvore website. Our
approach obtains 3-5% improvement over the state-of-the-art on outfit
compatibility prediction and fill-in-the-blank tasks using our dataset, as well
as an established smaller dataset, while supporting a variety of useful
queries.
