Paper ID: 2412.14521 • Published Dec 19, 2024
Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
Runsheng Zhang (1), Shixiao Wang (2), Tianfang Xie (3), Shiyu Duan (4), Mengmeng Chen (5) ((1) University of Southern California, (2)...
TL;DR
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This study presents a novel approach for intelligent user interaction
interface generation and optimization, grounded in the variational autoencoder
(VAE) model. With the rapid advancement of intelligent technologies,
traditional interface design methods struggle to meet the evolving demands for
diversity and personalization, often lacking flexibility in real-time
adjustments to enhance the user experience. Human-Computer Interaction (HCI)
plays a critical role in addressing these challenges by focusing on creating
interfaces that are functional, intuitive, and responsive to user needs. This
research leverages the RICO dataset to train the VAE model, enabling the
simulation and creation of user interfaces that align with user aesthetics and
interaction habits. By integrating real-time user behavior data, the system
dynamically refines and optimizes the interface, improving usability and
underscoring the importance of HCI in achieving a seamless user experience.
Experimental findings indicate that the VAE-based approach significantly
enhances the quality and precision of interface generation compared to other
methods, including autoencoders (AE), generative adversarial networks (GAN),
conditional GANs (cGAN), deep belief networks (DBN), and VAE-GAN. This work
contributes valuable insights into HCI, providing robust technical solutions
for automated interface generation and enhanced user experience optimization.