Unknown
Designing novel interfaces is challenging. Designers typi- cally rely on experience or subjective judgment in the ab- sence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an ef- ficient tool for objective interface feature refinement. Specif- ically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface de- sign across diverse deployment environments. Experiment 1 evaluates the approach on a familiar 2D interface design problem: a map search and review use case. Adding a de- gree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based vir- tual reality. The approach is then demonstrated as a case study for a fundamentally new and unfamiliar interaction design problem: web-based augmented reality. Finally, we show how the model generated as an outcome of the refine- ment process can be used for user simulation and queried to deliver various design insights. CCS CONCEPTS • Human-centered computing →Systems and tools for interaction design.