Computational Model for Predicting User Aesthetic Preference for GUI using DCNNs

Abstract

Visual aesthetics is vital in determining the usability of the graphical user interface (GUI). It can strengthen the competitiveness of interactive online applications. Human aesthetic preferences for GUI are implicit and linked to various aspects of perception. In this study, an aesthetic GUI image database was constructed with 38,423 design works collected from Huaban.com, a popular social network website for art and design sharing, collection, and exhibition in China. The numbers of user collection and likes of each design work were used as the annotation to represent user preference levels. Deep convolutional neural networks were applied to evaluate the aesthetic preferences of GUIs, based on a large dataset of user interface design images with the ground-truth annotations. The experimental result indicated the feasibility of the proposed method, with a mean squared error (MSE) of 0.0222 for user collection prediction and an MSE of 0.0644 for user likes prediction in the best model performance of Squeeze-and-Excitation-VGG19 networks (SE-VGG19). This study aims to build a large aesthetic image database, and to explore a practical and objective evaluation model of GUI aesthetics.