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

TL;DR

This paper presents a reinforcement learning-based system for automatically generating adaptive user interfaces that personalize and optimize user experience through continuous feedback and data-driven adjustments.

Contribution

It introduces a novel adaptive interface generation method combining reinforcement learning with feedback mechanisms, advancing personalized HCI design.

Findings

System effectively adapts interfaces based on user feedback.

Improves click-through rate and user retention.

Demonstrates flexibility and personalization in interface design.

Abstract

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.