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Visually-aware recommender systems use visual signals present in theunderlying data to model the visual characteristics of items and users'preferences towards them. In the domain of clothing recommendation,incorporating items' visual information (e.g., product images) is particularlyimportant since clothing item appearance is often a critical factor ininfluencing the user's purchasing decisions. Current state-of-the-artvisually-aware recommender systems utilize image features extracted frompre-trained deep convolutional neural networks, however these extremelyhigh-dimensional representations are difficult to interpret, especially inrelation to the relatively low number of visual properties that may guideusers' decisions.  In this paper we propose a novel approach to personalized clothingrecommendation that models the dynamics of individual users' visualpreferences. By using interpretable image representations generated with aunique feature learning process, our model learns to explain users' priorfeedback in terms of their affinity towards specific visual attributes andstyles. Our approach achieves state-of-the-art performance on personalizedranking tasks, and the incorporation of interpretable visual features allowsfor powerful model introspection, which we demonstrate by using an interactiverecommendation algorithm and visualizing the rise and fall of fashion trendsover time.

Visually-Aware Personalized Recommendation using Interpretable Image Representations