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

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arxiv 2412.16837 v1 pith:HV5ROEQC submitted 2024-12-22 cs.HC

Qi Sun , Yayun Xue , Zhijun Song This is my paper · ORCID
classification cs.HC
keywords interfaceuseradaptiveapproachdesigngenerationinteractionlearning
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 3 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Fatigue-Aware Adaptive Interfaces for Wearable Devices Using Deep Learning

    cs.LG 2025-06 reject novelty 4.0 of 10

    A fatigue-aware adaptive interface that combines multimodal physiological sensing with reinforcement learning is claimed to cut cognitive load by 18% and boost satisfaction by 22%, though the reported results are not ...

  2. A Deep Learning Approach to Interface Color Quality Assessment in HCI

    cs.HC 2025-02 reject novelty 3.0 of 10

    The authors train a CNN on website screenshots to predict user ratings of color quality and report high agreement, but provide no architecture, dataset size, or held-out validation.

  3. Graph Neural Network-Driven Hierarchical Mining for Complex Imbalanced Data

    cs.LG 2025-02 reject novelty 3.0 of 10

    The paper claims that GNN embeddings plus hierarchical mining improve frequent-pattern discovery for minority classes on imbalanced tabular data.

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