Personality modeling from image aesthetic attribute-aware graph representation learning.

Recently, inferring users' personality traits on social media has attracted extensive attention. Existing studies have shown that users' personality traits can be inferred from their preferences for images. However, since users' preferences on images are often affected by multiple factors, some liked images cannot effectively reflect their personality traits. To handle this issue, this paper proposes a personality modeling approach based on image aesthetic attribute-aware graph representation learning, which can leverage aesthetic attributes to refine the liked images that are consistent with users' personality traits. Specifically, we first utilize a Convolutional Neural Network (CNN) to train an aesthetic attribute prediction module. Then, attribute-aware graph representation learning is introduced to refine the images with similar aesthetic attributes from users' liked images. Finally, the aesthetic attributes of all refined images are combined to predict personality traits through a Multi-Layer Perceptron (MLP). Experimental results and visual analysis have shown that the proposed method is superior to state-of-the-art personality modeling methods. • We propose an attribute-aware graph representation learning for personality modeling. • The images with similar aesthetic attributes can be refined from users' liked images. • The combined aesthetic attributes of refined images are used to infer personality. [ABSTRACT FROM AUTHOR]