Abstract

Leveraging the power of computational methods, AI can perform effective strategies in intelligent design. Researchers are pushing the boundaries of AI, developing computational systems to solve complex questions. The authors investigate the association of user preference for UI and deep image features, aiming to predict user preference level using deep convolutional neural networks (DCNNs) trained on a UI design image dataset. A total of 12,186 UI design images were collected from UI.cn and DOOOOR.com. Users' views and likes can help understand the implicit user preference level, which is set as the ground-truth annotation for the dataset. Six DCNNs, including VGG-19, InceptionNet-V3, MobileNet, EfficientNet, ResNet-50 and NASNetLarge were trained to learn the user preference of UI images. The experiment achieves an optimal result with a mean-squared error of 0.000214 and a mean absolute error of 0.0103 based on EfficientNet, which indicates that the proposed method provides the possibility in learning the pattern of user aesthetics preference for UI design. On the basis of the prediction model, a mobile application named ‘HotUI’ was developed for UI design recommendations.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.