  Building successful recommender systems requires uncovering the underlying
dimensions that describe the properties of items as well as users' preferences
toward them. In domains like clothing recommendation, explaining users'
preferences requires modeling the visual appearance of the items in question.
This makes recommendation especially challenging, due to both the complexity
and subtlety of people's 'visual preferences,' as well as the scale and
dimensionality of the data and features involved. Ultimately, a successful
model should be capable of capturing considerable variance across different
categories and styles, while still modeling the commonalities explained by
`global' structures in order to combat the sparsity (e.g. cold-start),
variability, and scale of real-world datasets. Here, we address these
challenges by building such structures to model the visual dimensions across
different product categories. With a novel hierarchical embedding architecture,
our method accounts for both high-level (colorfulness, darkness, etc.) and
subtle (e.g. casualness) visual characteristics simultaneously.
