An adaptable UI/UX considering user’s cognitive and behavior information in distributed environment

Hyesung Ji, Youdong Yun, Seolhwa Lee, Kuekyeng Kim, Heui Seok Lim

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

User interface (UI) and user experience (UX) is the first thing users come to interact when accessing internet services. However, UI/UX, which did not put users into consideration when designed, causes many inconveniences. In order to provide a customized UI/UX, the accurate analysis of users are important and must be optimized for users through continuous updates. In this paper, we propose a method of analyzing the user’s cognitive and behavioral information in a distributed environment, providing a customized UI/UX based on the analysis. The proposed method measures the user’s cognitive ability and generates an initial profile, then provides a custom UI/UX by modifying the profile based on the user’s behavioral information. To generate the user profile, the cognitive response measurement and modeling of 122 users were performed and the proposed model was evaluated by 200 users. As a result of the experiment, overall satisfying results were obtained.

Original languageEnglish
JournalCluster Computing
Volume21
Issue number1
DOIs
Publication statusPublished - 2017 Jun 16

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User interfaces
Internet
Experiments

Keywords

  • Cognitive
  • Personalized service
  • User behavior
  • User experience
  • User interface
  • User model

ASJC Scopus subject areas

  • Software
  • Computer Networks and Communications

Cite this

An adaptable UI/UX considering user’s cognitive and behavior information in distributed environment. / Ji, Hyesung; Yun, Youdong; Lee, Seolhwa; Kim, Kuekyeng; Lim, Heui Seok.

In: Cluster Computing, Vol. 21, No. 1, 16.06.2017.

Research output: Contribution to journalArticle

Ji, Hyesung ; Yun, Youdong ; Lee, Seolhwa ; Kim, Kuekyeng ; Lim, Heui Seok. / An adaptable UI/UX considering user’s cognitive and behavior information in distributed environment. In: Cluster Computing. 2017 ; Vol. 21, No. 1.
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