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Crowdsourcing interface feature design with Bayesian optimization

Accepted version
Peer-reviewed

Type

Conference Object

Change log

Authors

Dudley, JJ 
Jacques, JT 
Kristensson, PO 

Abstract

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.

Description

Keywords

Interface Design, Optimization, Crowdsourcing

Journal Title

Conference on Human Factors in Computing Systems - Proceedings

Conference Name

CHI '19: CHI Conference on Human Factors in Computing Systems

Journal ISSN

Volume Title

Publisher

ACM
Sponsorship
Engineering and Physical Sciences Research Council (EP/R004471/1)