  The use of computational methods to evaluate aesthetics in photography has
gained interest in recent years due to the popularization of convolutional
neural networks and the availability of new annotated datasets. Most studies in
this area have focused on designing models that do not take into account
individual preferences for the prediction of the aesthetic value of pictures.
We propose a model based on residual learning that is capable of learning
subjective, user specific preferences over aesthetics in photography, while
surpassing the state-of-the-art methods and keeping a limited number of
user-specific parameters in the model. Our model can also be used for picture
enhancement, and it is suitable for content-based or hybrid recommender systems
in which the amount of computational resources is limited.
