Filtered by themeAdaptation usability trade-off5 papersclear ✕

Adapting User Interfaces with Model-based Reinforcement LearningKashyap Todi · 2021Adapting an interface requires taking into account both the positive and negative efects that changes may have on the user. A carelessly picked adaptation may impose high costs to the user – for example, due to surprise or relearning efort – or “trap” the process to a suboptimal design immaturely. However, efects on users are hard to predict as they depend on factors that are latent and evolve over the course of interaction. We propose a novel approach for adaptive user interfaces that yields a conservative adaptation policy: It fnds benefcial changes when there are such and avoids changes when there are none. Our model-based reinforcement learning Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for proft or commercial advantage and that copies bear this notice and the full citation on the frst page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specifc permission and/or a fee. Request permissions from permissions@acm.org. CHI ’21, May 8–13, 2021, Yokohama, Japan © 2021 Association for Computing Machinery. ACM ISBN 978-1-4503-8096-6/21/05...$15.00 https://doi.org/10.1145/3411764.3445497 method plans sequences of adaptations and consults predictive HCI models to estimate their efects. We present empirical and simulation results from the case of adaptive menus, showing that the method outperforms both a non-adaptive and a frequency-based policy. CCS CONCEPTS • Human-centered computing → Interactive systems and tools.

UX of AI & PersonalizationResearch Papers

Retroactive Transfer Phenomena inAlternating User Interfaces · 2020We investigated retroactive transfer when users alternate be­ tween different interfaces. Retroactive transfer is the influence of a newly learned interface on users’ performance with a pre­ viously learned interface. In an interview study, participants described their experiences when alternating between different interfaces, e.g. different operating systems, devices or tech­ niques. Negative retroactive transfer related to text entry was the most frequently reported incident. We then reported a lab­ oratory experiment that investigated the impact of similarity between two abstract keyboard layouts, and the number of alternations between them, on retroactive interference. Results indicated that even small changes in the interference interface produced a significant performance drop for the entire previ­ ously learned interface. The amplitude of this performance drop decreases with the number of alternations. We suggest that retroactive transfer should receive more attention in HCI, as the ubiquitous nature of interactions across applications and systems requires users to increasingly alternate between similar interfaces. Author Keywords skill transfer; retroactive interference; keyboard layout CCS Concepts •Human-centered computing → User studies;

UX of AI & PersonalizationResearch Papers

Familiarisation: Restructuring Layouts with Visual Learning ModelsKashyap Todi · 2018In domains where users are exposed to large variations in visuo-spatial features among designs, they often spend excess time searching for common elements (features) in familiar locations. This paper contributes computational approaches to restructuring layouts such that features on a new, unvisited interface can be found quicker. We explore four concepts of fa- miliarisation, inspired by the human visual system (HVS), to automatically generate a familiar design for each user. Given a history of previously visited interfaces, we restructure the spatial layout of the new (unseen) interface with the goal of making its elements more easily found. Familiariser is a browser-based implementation that automatically restruc- tures webpage layouts based on the visual history of the user. Our evaluation with users provides first evidence favouring familiarisation. Author Keywords Visual search; Graphical layouts; Computational design; Adaptive user interfaces

UX of AI & PersonalizationResearch Papers

The Influence of Personality Traits and Cognitive Load on the Use of Adaptive User InterfacesHarvard SEAS · 2017One of the problems adaptive interfaces must solve is the is- sue of stability—users must be able to complete a familiar task reliably. Split Adaptive Interfaces, where a limited part of the screen contains copies of the interface elements pre- dicted to be of immediate use, are one technique for resolv- ing this difficulty. While prior work demonstrated that Split Adaptive Interfaces improve performance on average, the re- sults of our study demonstrate systematic individual differ- ences in the utilization of the adaptive features, which cor- relate with the stable user traits of Need for Cognition and Extraversion. Specifically, higher Need for Cognition (a will- ingness to undertake difficult mental activities) is correlated with increased utilization rates, while higher Extraversion (a general orientation towards seeking gratification from the ex- ternal world) is negatively correlated with utilization rates. Our results also demonstrate a significant negative correlation between cognitive load induced by a secondary task and the utilization of the adaptive features. This effect, however, is very small (less than two percentage points). Together, these results provide additional evidence of the usefulness of the split adaptive interface approach and a negligible effect of ad- ditional cognitive load, but also demonstrate that the approach does not benefit all users equally. Author Keywords Adaptive user interfaces, cognitive load, extraversion, need for cognition ACM Classification Keywords H.5.m. Information Interfaces and Presentation: Miscella- neous

UX of AI & PersonalizationResearch Papers

Predictability and Accuracy in Adaptive User InterfacesKrzysztof Z. Gajos, Katherine Everitt et al. · 2008While proponents of adaptive user interfaces tout potential performance gains, critics argue that adaptation's unpredict- ability may disorient users, causing more harm than good. We present a study that examines the relative effects of predictability and accuracy on the usability of adaptive UIs. Our results show that increasing predictability and accuracy led to strongly improved satisfaction. Increasing accuracy also resulted in improved performance and higher utiliza- tion of the adaptive interface. Contrary to our expectations, improvement in accuracy had a stronger effect on perform- ance, utilization and some satisfaction ratings than the im- provement in predictability. AUTHOR KEYWORDS Adaptive interfaces, predictability, accuracy, user study ACM Classification Keywords H5.2 Information Interfaces and Presentation: User Inter- faces – Interactions Styles, Evaluation/Methodology

UX of AI & PersonalizationResearch Papers