Adaptive User Interface Generation Through Reinforcement Learning: A Data-Driven Approach to Personalization and Optimization

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Dades bibliogràfiques
Publicat a:	arXiv.org (Dec 22, 2024), p. n/a
Autor principal:	Sun, Qi
Altres autors:	Xue, Yayun, Song, Zhijun
Publicat:	Cornell University Library, arXiv.org
Matèries:	User interface User experience Adaptive systems Configuration management Human-computer interface Design optimization Human-computer interaction Feedback Customization
Accés en línia:	Citation/Abstract Full text outside of ProQuest
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Descripció
Resum:	This study introduces an adaptive user interface generation technology, emphasizing the role of Human-Computer Interaction (HCI) in optimizing user experience. By focusing on enhancing the interaction between users and intelligent systems, this approach aims to automatically adjust interface layouts and configurations based on user feedback, streamlining the design process. Traditional interface design involves significant manual effort and struggles to meet the evolving personalized needs of users. Our proposed system integrates adaptive interface generation with reinforcement learning and intelligent feedback mechanisms to dynamically adjust the user interface, better accommodating individual usage patterns. In the experiment, the OpenAI CLIP Interactions dataset was utilized to verify the adaptability of the proposed method, using click-through rate (CTR) and user retention rate (RR) as evaluation metrics. The findings highlight the system's ability to deliver flexible and personalized interface solutions, providing a novel and effective approach for user interaction design and ultimately enhancing HCI through continuous learning and adaptation.
ISSN:	2331-8422
Font:	Engineering Database