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 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.
Code (0)
등록된 구현이 없습니다.
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Characterizing and modeling harms from interactions with design patterns in AI interfaces
The proliferation of applications using artificial intelligence (AI) systems has led to a growing number of users interacting with these systems through sophisticated interfaces. Human-computer interaction research has l…
Language ModellingRecommendation SystemsInsights into User Interface Innovations from a Design Thinking Workshop at deRSE25
Large Language Models have become widely adopted tools due to their versatile capabilities, yet their user interfaces remain limited, often following rigid, linear interaction paradigms. In this paper, we present insight…
Enhanced Web User Interface Design Via Cross-Device Responsiveness Assessment Using An Improved HCI-INTEGRATED DL Schemes
User Interface (UI) optimization is essential in the digital era to enhance user satisfaction in web environments. Nevertheless, the existing UI optimization models had overlooked the Cross-Responsiveness (CR) assessment…
Optimizing Computer-Assisted Transcription Quality with Iterative User Interfaces
Computer-assisted transcription promises high-quality speech transcription at reduced costs. This is achieved by limiting human effort to transcribing parts for which automatic transcription quality is insufficient. Our …
A Large Language Model Enhanced Conversational Recommender System
Conversational recommender systems (CRSs) aim to recommend high-quality items to users through a dialogue interface. It usually contains multiple sub-tasks, such as user preference elicitation, recommendation, explanatio…
Language ModelingLanguage ModellingLarge Language Modelmodel+2