Abstract
User Interface (UI) aesthetic evaluation is a crucial task in designing products, websites, and mobile applications. Due to its dependence on individual experiences and preferences, UI evaluation is a complex and subjective process. Traditional methods typically rely on experts’ ideas or general feedback and do not account for the personal characteristics and features of users. This research introduces UIRate, a tool for UI aesthetic scoring based on user-personalized data and visual features of a UI. A dual-input model that contains an EfficientNet-based model for visual feature extraction and a multi-layer perceptron (MLP) for user feature processing was utilized. Furthermore, Grad-CAM was used as an explainability method, which provides interpretable insights about the important and unimportant areas in a UI for the predictions. In a user study with 10 professionals in the UI design sector, the results show that the model outperforms human experts at predicting scores. In addition, the positive feedback of UI designers confirms the applicability of UIRate for improvements in design. This tool is publicly available on Hugging Face and can be used for further research in the Human-Computer Interaction community.