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.

翻译：艺术品推荐具有挑战性，因为需要理解用户如何与高度主观的内容互动、艺术品中蕴含概念的复杂性，以及它们可能引发的用户情感与认知反应。本文聚焦于高效捕捉视觉艺术的要素（即潜在语义关系）以实现个性化推荐。我们提出并研究了基于文本与视觉特征学习技术及其组合的推荐系统，随后进行了小规模与大规模用户中心化推荐质量评估。结果表明，文本特征相较于视觉特征表现更优，而两者的融合能捕捉到最适合艺术品推荐的潜在语义关系。最终，本文深化了关于如何传递恰当匹配用户兴趣的内容及其感知方式的理解。