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.

翻译：本研究提出了一种基于变分自编码器（VAE）模型的智能用户交互界面生成与优化新方法。随着智能技术的快速发展，传统界面设计方法难以满足日益增长的多样化和个性化需求，且在实时调整以提升用户体验方面往往缺乏灵活性。人机交互（HCI）通过专注于创建功能性强、直观且响应用户需求的界面，在应对这些挑战中发挥着关键作用。本研究利用RICO数据集训练VAE模型，从而能够模拟和创建符合用户审美与交互习惯的用户界面。通过整合实时用户行为数据，系统动态地细化和优化界面，提升了可用性，并凸显了HCI在实现无缝用户体验中的重要性。实验结果表明，与自编码器（AE）、生成对抗网络（GAN）、条件生成对抗网络（cGAN）、深度信念网络（DBN）以及VAE-GAN等其他方法相比，基于VAE的方法显著提高了界面生成的质量与精度。这项工作为HCI领域贡献了有价值的见解，为自动化界面生成和增强的用户体验优化提供了坚实的技术解决方案。