Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders

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 user experience. Human-Computer Interaction (HCI) plays a critical role in addressing these challenges, as it focuses on creating interfaces that are not only functional but also 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. Through the integration of real-time user behavior data, the system dynamically refines and optimizes the interface to improve usability, thereby underscoring the importance of HCI in achieving a seamless user experience. The 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 generative adversarial networks (cGAN), deep belief networks (DBN), and variational autoencoder generative adversarial networks (VAE-GAN). This work contributes valuable insights into Human-Computer Interaction (HCI), providing robust technical solutions for automated interface generation and enhanced user experience optimization.