Familiarisation: Restructuring layouts with visual learning models

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12 Citations (Scopus)
81 Downloads (Pure)

Abstract

In 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 familiarisation, 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 restructures webpage layouts based on the visual history of the user. Our evaluation with users provides first evidence favouring familiarisation.

Original languageEnglish
Title of host publicationIUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces
PublisherACM
Pages547-558
Number of pages12
VolumePart F135193
ISBN (Electronic)9781450349451
DOIs
Publication statusPublished - 5 Mar 2018
MoE publication typeA4 Article in a conference publication
EventInternational Conference on Intelligent User Interfaces - Tokyo, Japan
Duration: 7 Mar 201811 Mar 2018
Conference number: 23
http://iui.acm.org/2018/index.html

Conference

ConferenceInternational Conference on Intelligent User Interfaces
Abbreviated titleIUI
CountryJapan
CityTokyo
Period07/03/201811/03/2018
Internet address

Keywords

  • Adaptive user interfaces
  • Computational design
  • Graphical layouts
  • Visual search

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