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

arXiv: https://arxiv.org/abs/2412.16837 · HTML: https://mlsanity.com/paper/2412.16837/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2024.

## Built on by (most cited first, 4 of 4)

- [Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data](https://mlsanity.com/paper/2502.03803/) (2025)
- [Multi-Level Attention and Contrastive Learning for Enhanced Text Classification with an Optimized Transformer](https://mlsanity.com/paper/2501.13467/) (2025)
- [Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning](https://mlsanity.com/paper/2506.13203/) (2025)
- [Intelligent Front-End Personalization: AI-Driven UI Adaptation](https://mlsanity.com/paper/2602.03154/) (2026)

## Abstract (arXiv)

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.