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arXiv · 1809.00060

Aesthetic Features for Personalized Photo Recommendation

Yu Qing Zhou·Ga Wu·Scott Sanner·Putra Manggala
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Abstract

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.

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Yu Qing Zhou, Ga Wu, Scott Sanner, Putra Manggala. 2018-08-31. Aesthetic Features for Personalized Photo Recommendation. https://arxiv.org/abs/1809.00060

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