Collaborative filtering of color aesthetics

@inproceedings{ODonovan2014CollaborativeFO,
  title={Collaborative filtering of color aesthetics},
  author={Peter O'Donovan and Aseem Agarwala and Aaron Hertzmann},
  booktitle={CAe '14},
  year={2014}
}
  • Peter O'Donovan, Aseem Agarwala, Aaron Hertzmann
  • Published in CAe '14 2014
  • Computer Science
  • 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… CONTINUE READING

    Create an AI-powered research feed to stay up to date with new papers like this posted to ArXiv

    Figures, Tables, and Topics from this paper.

    Citations

    Publications citing this paper.
    SHOWING 1-10 OF 13 CITATIONS

    Personalized Image Aesthetics

    VIEW 6 EXCERPTS
    CITES METHODS & BACKGROUND
    HIGHLY INFLUENCED

    Personalised aesthetics with residual adapters

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Towards color compatibility in fashion using machine learning

    VIEW 2 EXCERPTS
    CITES BACKGROUND

    Color Orchestra: Ordering Color Palettes for Interpolation and Prediction

    VIEW 1 EXCERPT
    CITES BACKGROUND

    Thoughts and Tools for Crafting Colors: Implications from Designers' Behavior

    VIEW 1 EXCERPT
    CITES BACKGROUND

    References

    Publications referenced by this paper.