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

TL;DR

This paper introduces a VAE-based system for dynamic, personalized user interface generation that adapts in real-time to user behavior, significantly improving interface quality and user experience.

Contribution

It presents a novel VAE-driven approach for real-time, adaptive interface generation, outperforming existing generative models in HCI applications.

Findings

VAE approach outperforms AE, GAN, cGAN, DBN, and VAE-GAN in interface quality.

Real-time user data integration improves interface personalization.

Enhanced usability and user satisfaction demonstrated through experiments.

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.