Efficient Personalization of Generative User Interfaces
Generative user interfaces (GenUIs) create new opportunities to adapt interfaces to individual users on demand. Yet personalization is difficult because it is not possible to provide settings for screens that have not yet been generated, making it necessary to learn preferences from users' feedback on generated interfaces. Such feedback is sparse, subjective, and often difficult to articulate. We study this problem through a new dataset in which 20 participants each judge the same 600 pairs of GenUIs. Their judgments differ substantially (Krippendorff's alpha = 0.25), and written rationales show that even when participants consider similar UI properties, such as color tone or hierarchy, t...
| Authors | Yi-Hao Peng, Jeffrey P. Bigham, Jason Wu |
|---|---|
| Primary category | cs.LG |
| Published | 10 April 2026 |
| Last updated at source | 14 September 2026 |
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