  Understanding users' interactions with highly subjective content---like
artistic images---is challenging due to the complex semantics that guide our
preferences. On the one hand one has to overcome `standard' recommender systems
challenges, such as dealing with large, sparse, and long-tailed datasets. On
the other, several new challenges present themselves, such as the need to model
content in terms of its visual appearance, or even social dynamics, such as a
preference toward a particular artist that is independent of the art they
create.
  In this paper we build large-scale recommender systems to model the dynamics
of a vibrant digital art community, Behance, consisting of tens of millions of
interactions (clicks and `appreciates') of users toward digital art.
Methodologically, our main contributions are to model (a) rich content,
especially in terms of its visual appearance; (b) temporal dynamics, in terms
of how users prefer `visually consistent' content within and across sessions;
and (c) social dynamics, in terms of how users exhibit preferences both towards
certain art styles, as well as the artists themselves.
