Many photography websites such as Flickr, 500px, Unsplash, and Adobe Behance are used by amateur and professional photography enthusiasts. Unlike content-based image search, such users of photography websites are not just looking for photos with certain content, but more generally for photos with a certain photographic "aesthetic". In this context, we explore personalized photo recommendation and propose two aesthetic feature extraction methods based on (i) color space and (ii) deep style transfer embeddings. Using a dataset from 500px, we evaluate how these features can be best leveraged by collaborative filtering methods and show that (ii) provides a significant boost in photo recommendation performance.

翻译：许多摄影网站,如Flickr、500px、Unspsplash和Adobe Behance都为业余和专业摄影爱好者所使用。 与基于内容的图像搜索不同,这些摄影网站的用户不仅寻找带有某些内容的照片,而且更一般地寻找带有某种图片“美学”的照片。 在这方面,我们探索了个性化的照片建议,并根据(一) 颜色空间和(二) 深风格传输嵌入,提出了两种美学特征提取方法。 我们使用来自500px的数据集,评估了如何通过合作过滤方法最佳利用这些特征,并展示了(二) 照片建议性能的显著提高。