Practical approaches to group-level multi-objective Bayesian optimization in interaction technique design

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openAccess

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A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä

Date

2024-01

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en

Pages

19

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Collective intelligence, Volume 3, issue 1

Abstract

Designing interaction techniques for end-users often involves exploring vast design spaces while balancing many objectives. Bayesian optimization offers a principled human-in-the-loop method for selecting designs for evaluation to efficiently explore such design spaces. To date, the application of Bayesian optimization in a human-in-the-loop setting has largely been restricted to optimization, or customization, of interaction techniques for individual user needs. In practice, interaction techniques are typically designed for a target population or group of users, with the goal is to produce a design that works well for most users. To accommodate this common use case in interaction technique design, we introduce two practical approaches that facilitate multi-objective Bayesian optimization at the group level. Specifically, our approaches streamline the process of (1) deriving designs suitable for a group of users from data collected in individual user evaluations; and (2) deriving an initialization from group data to improve the efficiency of design optimization for new users. We demonstrate the advantages of these practical approaches in two multi-phase user studies involving the design of non-trivial interaction techniques.

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Liao, Y-C, Mo, G B, Dudley, J J, Cheng, C L, Chan, L, Kristensson, P O & Oulasvirta, A 2024, ' Practical approaches to group-level multi-objective Bayesian optimization in interaction technique design ', Collective Intelligence, vol. 3, no. 1 . https://doi.org/10.1177/26339137241241313