Leveraging Content-Style Item Representation for Visual Recommendation

When customers' choices may depend on the visual appearance of products (e.g., fashion), visually-aware recommender systems (VRSs) have been shown to provide more accurate preference predictions than pure collaborative models. To refine recommendations, recent VRSs have tried to recognize the influence of each item's visual characteristic on users' preferences, for example, through attention mechanisms. Such visual characteristics may come in the form of content-level item metadata (e.g., image tags) and reviews, which are not always and easily accessible, or image regions-of-interest (e.g., the collar of a shirt), which miss items' style. To address these limitations, we propose a pipeline for visual recommendation, built upon the adoption of those features that can be easily extracted from item images and represent the item content on a stylistic level (i.e., color, shape, and category of a fashion product). Then, we inject such features into a VRS that exploits attention mechanisms to uncover users' personalized importance for each content-style item feature and a neural architecture to model non-linear patterns within user-item interactions. We show that our solution can reach a competitive accuracy and beyond-accuracy trade-off compared with other baselines on two fashion datasets. Code and datasets are available at: https://github.com/sisinflab/Content-Style-VRSs.