The Elements of Visual Art Recommendation: Learning Latent Semantic Representations of Paintings
This work addresses personalized recommendation for users of visual art, but it is incremental as it applies existing feature learning techniques to a specific domain.
The paper tackled the challenge of artwork recommendation by comparing textual, visual, and combined features to capture latent semantic relationships, finding that a fusion of both yields the best results in user-centric evaluations.
Artwork recommendation is challenging because it requires understanding how users interact with highly subjective content, the complexity of the concepts embedded within the artwork, and the emotional and cognitive reflections they may trigger in users. In this paper, we focus on efficiently capturing the elements (i.e., latent semantic relationships) of visual art for personalized recommendation. We propose and study recommender systems based on textual and visual feature learning techniques, as well as their combinations. We then perform a small-scale and a large-scale user-centric evaluation of the quality of the recommendations. Our results indicate that textual features compare favourably with visual ones, whereas a fusion of both captures the most suitable hidden semantic relationships for artwork recommendation. Ultimately, this paper contributes to our understanding of how to deliver content that suitably matches the user's interests and how they are perceived.