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Understanding users' interactions with highly subjective content---likeartistic images---is challenging due to the complex semantics that guide ourpreferences. On the one hand one has to overcome `standard' recommender systemschallenges, such as dealing with large, sparse, and long-tailed datasets. Onthe other, several new challenges present themselves, such as the need to modelcontent in terms of its visual appearance, or even social dynamics, such as apreference toward a particular artist that is independent of the art theycreate.  In this paper we build large-scale recommender systems to model the dynamicsof a vibrant digital art community, Behance, consisting of tens of millions ofinteractions (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 termsof how users prefer `visually consistent' content within and across sessions;and (c) social dynamics, in terms of how users exhibit preferences both towardscertain art styles, as well as the artists themselves.

Vista: A Visually, Socially, and Temporally-aware Model for Artistic Recommendation