Adapting User Interfaces with Model-based Reinforcement Learning

Todi, Kashyap; Bailly, Gilles; Leiva, Luis A.; Oulasvirta, Antti

doi:10.1145/3411764.3445497

Cited by 52 publications

(23 citation statements)

References 58 publications

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“…One way of realizing this support could entail using MutationObserver [46] to track DOM changes and applying layout adaptation only after client-side rendering is complete. Also, incorporating the latest advancements in the field of adaptive user interfaces would enhance the adaptation strategy so as to consider both end-user costs and benefits [35].…”

Section: Discussionmentioning

confidence: 99%

Responsive and Personalized Web Layouts with Integer Programming

Laine

Santala

Jokinen

et al. 2021

Proc. ACM Hum.-Comput. Interact.

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Over the past decade, responsive web design (RWD) has become the de facto standard for adapting web pages to a wide range of devices used for browsing. While RWD has improved the usability of web pages, it is not without drawbacks and limitations: designers and developers must manually design the web layouts for multiple screen sizes and implement associated adaptation rules, and its "one responsive design fits all" approach lacks support for personalization. This paper presents a novel approach for automated generation of responsive and personalized web layouts. Given an existing web page design and preferences related to design objectives, our integer programming -based optimizer generates a consistent set of web designs. Where relevant data is available, these can be further automatically personalized for the user and browsing device. The paper includes presentation of techniques for runtime adaptation of the designs generated into a fully responsive grid layout for web browsing. Results from our ratings-based online studies with end users (N = 86) and designers (N = 64) show that the proposed approach can automatically create high-quality responsive web layouts for a variety of real-world websites.

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Section: Discussionmentioning

confidence: 99%

Responsive and Personalized Web Layouts with Integer Programming

Laine

Santala

Jokinen

et al. 2021

Proc. ACM Hum.-Comput. Interact.

Self Cite

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show abstract

“…DQNs have been tested in various complicated tasks and were able to outperform all previous RL algorithms [Silver et al 2016[Silver et al , 2017. DQNs have also enabled breakthroughs such as "AlphaGO" [Chen 2016] and"AlphaStar" [Arulkumaran et al 2019], which have inspired recent work on AUIs in the context of linear menus [Todi et al 2021]. These advancements demonstrate the potential of RL to build intelligent agents by giving them the freedom to learn by exploring their environment and make decisions to take actions that maximise a long term reward.…”

Section: A Contextual Framework For Auismentioning

confidence: 99%

A Contextual Framework for Adaptive User Interfaces: Modelling the Interaction Environment

Dubiel¹,

Yilma²,

Latifzadeh³

et al. 2022

Preprint

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The interaction context (or environment) is key to any HCI task and especially to adaptive user interfaces (AUIs), since it represents the conditions under which users interact with computers. Unfortunately, there are currently no formal representations to model said interaction context. In order to address this gap, we propose a contextual framework for AUIs and illustrate a practical application using learning management systems as a case study. We also discuss limitations of our framework and offer discussion points about the realisation of truly context-aware AUIs. CCS CONCEPTS• Human-centered computing → Interactive systems and tools; HCI theory, concepts and models; Ubiquitous and mobile computing theory, concepts and paradigms.

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“…Why not mix the best of both fields by feeding classical or new models with users' data abstracted by means of ML techniques [27], thus obtaining "grey models"? A representative example is a model-based reinforcement learning approach proposed by Todi et al [38], which plans a sequence of adaptation steps (instead of a one-shot adaptation) and exploits a model to assess the cost/benefit ratio. 3.…”

Section: Opportunities For the Modelling Communitymentioning

confidence: 99%

Model-based intelligent user interface adaptation: challenges and future directions

et al. 2021

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Adapting the user interface of a software system to the requirements of the context of use continues to be a major challenge, particularly when users become more demanding in terms of adaptation quality. A considerable number of methods have, over the past three decades, provided some form of modelling with which to support user interface adaptation. There is, however, a crucial issue as regards in analysing the concepts, the underlying knowledge, and the user experience afforded by these methods as regards comparing their benefits and shortcomings. These methods are so numerous that positioning a new method in the state of the art is challenging. This paper, therefore, defines a conceptual reference framework for intelligent user interface adaptation containing a set of conceptual adaptation properties that are useful for model-based user interface adaptation. The objective of this set of properties is to understand any method, to compare various methods and to generate new ideas for adaptation. We also analyse the opportunities that machine learning techniques could provide for data processing and analysis in this context, and identify some open challenges in order to guarantee an appropriate user experience for end-users. The relevant literature and our experience in research and industrial collaboration have been used as the basis on which to propose future directions in which these challenges can be addressed.

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Adapting User Interfaces with Model-based Reinforcement Learning

Cited by 52 publications

References 58 publications

Responsive and Personalized Web Layouts with Integer Programming

Responsive and Personalized Web Layouts with Integer Programming

A Contextual Framework for Adaptive User Interfaces: Modelling the Interaction Environment

Model-based intelligent user interface adaptation: challenges and future directions

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