Generative user interfaces (UIs) create new opportunities to adapt interfaces to individual users on demand, but personalization remains difficult because desirable UI properties are subjective, hard to articulate, and costly to infer from sparse feedback. We study this problem through a new dataset in which 20 trained designers each provide pairwise judgments over the same 600 generated UIs, enabling direct analysis of preference divergence. We find substantial disagreement across designers (average kappa = 0.25), and written rationales reveal that even when designers appeal to similar concepts such as hierarchy or cleanliness, designers differ in how they define, prioritize, and apply those concepts. Motivated by these findings, we develop a sample-efficient personalization method that represents a new user in terms of prior designers rather than a fixed rubric of design concepts. In a technical evaluation, our preference model outperforms both a pretrained UI evaluator and a larger multimodal model, and scales better with additional feedback. When used to personalize generation, it also produces interfaces preferred by 12 new designers over baseline approaches, including direct user prompting. Our findings suggest that lightweight preference elicitation can serve as a practical foundation for personalized generative UI systems.

翻译：生成式用户界面（UI）为按需适配个体用户创造了新机遇，但个性化仍面临挑战，因为理想的UI属性具有主观性、难以清晰表述，且从稀疏反馈中推断成本高昂。我们通过一个新数据集研究该问题：20位资深设计师对同一批600个生成式UI分别进行成对比较，从而直接分析偏好的差异性。研究发现设计师之间存在显著分歧（平均kappa系数为0.25），书面理由分析表明，即便设计师使用“层次结构”“整洁度”等相似概念，他们对这些概念的定义、优先级和应用方式仍存在差异。基于这些发现，我们开发了一种样本高效的个性化方法，通过前期设计师集合而非固定设计准则来表征新用户。技术评估显示，我们的偏好模型优于预训练UI评估器及更大的多模态模型，且随反馈增加展现出更好的扩展性。当将该方法用于个性化生成时，12位新设计师对生成界面的偏好程度超越基线方法（包括直接用户提示）。研究结果表明：轻量级偏好引导可作为个性化生成式UI系统的实用基础。