Modeling how different user groups perceive webpage aesthetics

Luis A. Leiva*, Morteza Shiripour, Antti Oulasvirta

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

4 Citations (Scopus)

Abstract

Aesthetics is a central consideration in user interface design. It is known to affect end-user behavior and perception, in particular the first impression of graphical user interfaces. However, what users perceive as pleasant or good design is highly subjective. We contribute a computational model that estimates the visual appeal of a given webpage for several common cohorts, or user groups, including gender, age, and education level. Our model, a convolutional neural network trained on 418 webpage screenshots having 771k aesthetic scores (in a 1–9 Likert scale) from 32k users, achieves high accuracy and is always less than 1 point off from ground-truth ratings. Designers can use our model to anticipate how people would rate their webpage, offer personalized designs according to the visual preferences of their users, and support rapid evaluations of webpage design prototypes for specific cohorts.

Original languageEnglish
Pages (from-to)1417-1424
Number of pages8
JournalUNIVERSAL ACCESS IN THE INFORMATION SOCIETY
Volume22
Issue number4
Early online date29 Aug 2022
DOIs
Publication statusPublished - Nov 2023
MoE publication typeA1 Journal article-refereed

Keywords

  • Computational Accessibility
  • Neural Networks
  • Visual Design
  • Webpage Aesthetics

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