Abstract
This paper proposes a new approach for online UI adaptation that aims to overcome the limitations of the most commonly used UI optimization method involving multiple objectives: weighted sum optimization. Weighted sums are highly sensitive to objective formulation, limiting the effectiveness of UI adaptations. We propose ParetoAdapt, an adaptation approach that uses online multi-objective optimization with a posteriori articulated preferences—that is, articulation of preferences after the optimization has concluded—to make UI adaptation robust to incomplete and inaccurate objective formulations. It offers users a flexible way to control adaptations by selecting from a set of Pareto optimal adaptation proposals and adjusting them to fit their needs. We showcase the feasibility and flexibility of ParetoAdapt by implementing an online layout adaptation system in a state-of-the-art 3D UI adaptation framework. We further evaluate its robustness and run-time in simulation-based experiments that allow us to systematically change the accuracy of the estimated user preferences. We conclude by discussing how our approach may impact the usability and practicality of online UI adaptations.
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@inproceedings{johns2024,
title = {Towards Flexible and Robust User Interface Adaptations With Multiple Objectives},
author = {Christoph Albert Johns and João Marcelo Evangelista Belo and Anna Maria Feit and Clemens Nylandsted Klokmose and Ken Pfeuffer},
url = {https://dl.acm.org/doi/abs/10.1145/3586183.3606799},
doi = {10.1145/3586183.3606799},
isbn = {9798400701320},
year = {2023},
date = {2023-01-01},
urldate = {2023-01-01},
booktitle = {Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology},
publisher = {Association for Computing Machinery},
address = {San Francisco, CA, USA},
series = {UIST '23},
abstract = {This paper proposes a new approach for online UI adaptation that aims to overcome the limitations of the most commonly used UI optimization method involving multiple objectives: weighted sum optimization. Weighted sums are highly sensitive to objective formulation, limiting the effectiveness of UI adaptations. We propose ParetoAdapt, an adaptation approach that uses online multi-objective optimization with a posteriori articulated preferences—that is, articulation of preferences after the optimization has concluded—to make UI adaptation robust to incomplete and inaccurate objective formulations. It offers users a flexible way to control adaptations by selecting from a set of Pareto optimal adaptation proposals and adjusting them to fit their needs. We showcase the feasibility and flexibility of ParetoAdapt by implementing an online layout adaptation system in a state-of-the-art 3D UI adaptation framework. We further evaluate its robustness and run-time in simulation-based experiments that allow us to systematically change the accuracy of the estimated user preferences. We conclude by discussing how our approach may impact the usability and practicality of online UI adaptations.},
keywords = {multi-objective optimization, online UI adaptation, Pareto frontier},
pubstate = {published},
tppubtype = {inproceedings}
}