A ResNet-Enhanced Framework for Modelling Aesthetic Preferences in User-Centred Decision Systems - Jiadi Liu; Yixin Zhang; Shuhui Liu (Decision Making: Applications in Management and Engineering, 2024)
Modelling user aesthetic preferences is essential in contemporary multimodal interaction systems, as it enhances content relevance, user engagement, and decision-making efficacy. This study introduces a deep learning-based framework that integrates visual feature extraction with user interaction data to estimate the aesthetic quality of multimedia content, using images and videos as illustrative cases. The framework utilises statistical image analysis as input to deep convolutional architectures, specifically Residual Neural Networks (ResNets), to compute key aesthetic attributes such as colour harmony, lightness, and visual complexity. Additionally, user engagement indicators, including likes and collections, are incorporated alongside these visual features to infer patterns of aesthetic preference. Given the inherent subjectivity and variability in user data, the framework applies annotation normalisation and layer-freezing strategies to enhance model generalisation and training efficiency. The proposed system demonstrates robust capabilities in aesthetic scoring and preference modelling, facilitating informed content presentation and decision support across various digital environments. This work contributes to the advancement of intelligent, user-aware systems within the domains of multimedia interaction and human-computer interface design