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

TL;DR

This paper introduces Vista, a comprehensive recommendation model that incorporates visual, social, and temporal factors to better understand user preferences in a large digital art community.

Contribution

The paper presents a novel large-scale recommender system that models visual content, temporal session dynamics, and social influences for artistic content.

Findings

Effective modeling of visual content improves recommendation accuracy.

Temporal dynamics reveal user preferences for visual consistency.

Social factors like artist and style preferences significantly impact recommendations.

Abstract

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.