Collaborative filtering of color aesthetics

Abstract

This paper investigates individual variation in aesthetic preferences, and learns models for predicting the preferences of individual users. Preferences for color aesthetics are learned using a collaborative filtering approach on a large dataset of rated color themes/palettes. To make predictions, matrix factorization is used to estimate latent vectors for users and color themes. We also propose two extensions to the probabilistic matrix factorization framework. We first describe a feature-based model using learned transformations from feature vectors to a latent space, then extend this model to non-linear transformations using a neural network. These extensions allow our model to predict preferences for color themes not present in the training set. We find that our approach for modelling user preferences outperforms an average aesthetic model which ignores personal variation. We also use the model for measuring theme similarity and visualizing the space of color themes.