Crowdsourcing interface feature design with Bayesian optimization

John J. Dudley, Jason T. Jacques, Per Ola Kristensson

Research output: Chapter in Book/Report/Conference proceedingConference contribution

24 Citations (Scopus)


Designing novel interfaces is challenging. Designers typically rely on experience or subjective judgment in the absence of analytical or objective means for selecting interface parameters. We demonstrate Bayesian optimization as an efficient tool for objective interface feature refinement. Specifically, we show that crowdsourcing paired with Bayesian optimization can rapidly and effectively assist interface design 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 degree of complexity, Experiment 2 extends Experiment 1 by switching the deployment environment to mobile-based virtual 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 refinement process can be used for user simulation and queried to deliver various design insights.

Original languageEnglish
Title of host publicationCHI 2019 - Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems
ISBN (Electronic)9781450359702
Publication statusPublished - 2 May 2019
Event2019 CHI Conference on Human Factors in Computing Systems, CHI 2019 - Glasgow, United Kingdom
Duration: 4 May 20199 May 2019

Publication series

NameConference on Human Factors in Computing Systems - Proceedings


Conference2019 CHI Conference on Human Factors in Computing Systems, CHI 2019
Country/TerritoryUnited Kingdom


  • Crowdsourcing
  • Interface design
  • Optimization


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