Familiarisation: Restructuring layouts with visual learning models

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.

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| openaire: EC/H2020/637991/EU//COMPUTED

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

DOI

10.1145/3172944.3172949

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Todi, K, Jokinen, J, Luyten, K & Oulasvirta, A 2018, Familiarisation : Restructuring layouts with visual learning models . in IUI 2018 - Proceedings of the 23rd International Conference on Intelligent User Interfaces . vol. Part F135193, ACM, pp. 547-558, International Conference on Intelligent User Interfaces, Tokyo, Japan, 07/03/2018 . https://doi.org/10.1145/3172944.3172949

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https://urn.fi/URN:NBN:fi:aalto-201809064964

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[article-cris] Sähkötekniikan korkeakoulu / ELEC

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Familiarisation: Restructuring layouts with visual learning models

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