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：艺术艺术建议具有挑战性,因为它要求了解用户如何与高度主观内容互动,艺术作品所含概念的复杂性,以及他们可能引起用户的情感和认知思考。在本文中,我们侧重于高效捕捉视觉艺术要素(即潜在的语义关系),以个人化建议。我们提出和研究基于文字和视觉特征学习技术及其组合的建议系统。然后我们对建议的质量进行小规模和大规模以用户为中心的评估。我们的结果显示,文字特征与视觉特征相比是有利的,而两者的融合则捕捉艺术建议最合适的隐藏语义关系。最终,本文件有助于我们了解如何提供适合用户利益和如何看待的内容。</s>