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Although there are several visually-aware recommendation models in domainslike fashion or even movies, the art domain lacks thesame level of researchattention, despite the recent growth of the online artwork market. To reducethis gap, in this article we introduceCuratorNet, a neural network architecturefor visually-aware recommendation of art images. CuratorNet is designed at thecore withthe goal of maximizing generalization: the network has a fixed set ofparameters that only need to be trained once, and thereafter themodel is ableto generalize to new users or items never seen before, without furthertraining. This is achieved by leveraging visualcontent: items are mapped toitem vectors through visual embeddings, and users are mapped to user vectors byaggregating the visualcontent of items they have consumed. Besides the modelarchitecture, we also introduce novel triplet sampling strategies to buildatraining set for rank learning in the art domain, resulting in more effectivelearning than naive random sampling. With an evaluationover a real-worlddataset of physical paintings, we show that CuratorNet achieves the bestperformance among several baselines,including the state-of-the-art model VBPR.CuratorNet is motivated and evaluated in the art domain, but its architectureand trainingscheme could be adapted to recommend images in other areas

CuratorNet: Visually-aware Recommendation of Art Images