Affective Color Palette Recommendations with Non-negative Tensor Factorization
Ikuya Morita, Shigeo Takahashi, Satoshi Nishimura, Kazuo Misue · 2022
Color is an essential factor that influences human perception, and thus, the proper selection of color sets is crucial in creating informative and appealing visual content. Furthermore, the choice of such color palettes often reflects the underlying emotional intention of creators, especially when they want to introduce specific affective styles. This paper presents a color palette recommendation system that facilitates preferred colors and affective expressions in visual content. This is accomplished by introducing non-negative tensor factorization (NTF), which extends the conventional matrix-based collaborative filtering for recommending items through ratings of multiple users. In our approach, we composed a rating tensor that constitutes the scores for colors in terms of affective factors provided by participants in the user study. With this rating tensor, we explored the meaningful relation between affective expression and color preference. Our experiments exposed that we can successfully apply a tensor-based approach to recommending convincing sets of colors in several possible cases by predicting the underlying emotional intentions in the visual content design.