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

• URL: http://arxiv.org/abs/2412.14521v1
• Date: Thu, 19 Dec 2024 04:37:47 GMT
• Title: Dynamic User Interface Generation for Enhanced Human-Computer Interaction Using Variational Autoencoders
• Authors: Runsheng Zhang, Shixiao Wang, Tianfang Xie, Shiyu Duan, Mengmeng Chen,
• Abstract summary: This study presents a novel approach for intelligent user interaction interface generation and optimization, grounded in the variational autoencoder (VAE) model.<n>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.
• Score: 4.1676654279172265
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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