Personalized Image Aesthetic Assessment based on Graph Neural Network and Collaborative Filtering

Personalized image aesthetics assessment aims to capture individual aesthetic preferences, which are influenced by image aesthetic attributes and user demographic attributes. The interaction of attributes facilitates the determination of users' aesthetic preferences for images. Therefore, we define two forms of attribute interactions: external-interactions and internal-interactions. The interaction of these two types of attributes is not considered in existing models. To address this drawback, we suggest a personalized image aesthetics assessment method based on graph neural network and collaborative filtering, which models and aggregates two types of attribute interactions in the graph structure for predicting personalized image aesthetics scores. Firstly, we designed an image aesthetic feature extraction phase for obtaining aesthetic attributes and distributions based on the aesthetic assessment of mass images. Secondly, we propose an aesthetic prior model-building phase with two basic processes: learning the aesthetic features of images and users' aesthetic viewpoints; learning users' preferences for images. This phase is accomplished through internal-interactions (using the graph's message passing mechanism) and external-interactions (using collaborative filtering). Finally, we fuse the post-interaction features and image aesthetic distribution features for personalized image aesthetic assessment. The performance of our designed method is outperformed by the state-of-the-art method, as seen from the experimental results. Furthermore, further studies verify the accuracy and validity of our model in providing improved prediction of users' aesthetic preferences.