Please use this identifier to cite or link to this item:
Title: Machine learning estimation of users’ implicit and explicit aesthetic judgments of web-pages
Authors: Gabrieli, Giulio
Bornstein, Marc H.
Setoh, Peipei
Esposito, Gianluca
Keywords: Social sciences::Psychology
Engineering::Computer science and engineering::Computer applications::Social and behavioral sciences
Issue Date: 2022
Source: Gabrieli, G., Bornstein, M. H., Setoh, P. & Esposito, G. (2022). Machine learning estimation of users’ implicit and explicit aesthetic judgments of web-pages. Behaviour & Information Technology.
Journal: Behaviour & Information Technology
Abstract: The aesthetic appearance of websites can influence the perception of their usability, reliability, and trustworthiness. A majority of studies investigating the relationship between aesthetic features of web pages and their user perception consider only a limited number of web pages’ visual features and focus exclusively on explicit aesthetic judgments. In this work, we aim to overcome the limitations of previous works by employing multiple visual features, as well as implicit aesthetic appreciation measures estimated by individuals’ neurophysiological activity. Furthermore we aim to study the ability of machine learning models to predict the aesthetic judgments of webpages. We also investigate the differences between the prediction accuracy of explicit and implicit judgments of web pages. Our approach, based on the analysis of physiological signals, uses machine learning and neural network models to estimate users’ implicit aesthetic pleasure. In our experiments, a group of young adults (N = 59, 33 females, Mean age = 21.52 years) assessed the aesthetic appeal of 100 web pages and 50 emotional pictures while we recorded their physiological activity. Our results demonstrate that machine learning models have a higher accuracy at predicting users’ explicit judgments, as compared to implicit judgments.
ISSN: 0144-929X
DOI: 10.1080/0144929X.2021.2023635
DOI (Related Dataset): 10.21979/N9/YCDXNE
Schools: School of Social Sciences 
Lee Kong Chian School of Medicine (LKCMedicine) 
Research Centres: Social and Affective Neuroscience Lab 
Rights: © 2022 Informa UK Limited, trading as Taylor & Francis Group. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:LKCMedicine Journal Articles
SSS Journal Articles

Citations 50

Updated on May 29, 2023

Web of ScienceTM
Citations 50

Updated on May 24, 2023

Page view(s)

Updated on Jun 1, 2023

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.