  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.
