About: How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data

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An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
Creator	Pappas, Ilias Giannakos, Michail Mikalef, Patrick Sharma, Kshitij
source	PMC
abstract	This study examines how quickly we can predict users’ ratings on visual aesthetics in terms of simplicity, diversity, colorfulness, craftsmanship. To predict users’ ratings, first we capture gaze behavior while looking at high, neutral, and low visually appealing websites, followed by a survey regarding user perceptions on visual aesthetics towards the same websites. We conduct an experiment with 23 experienced users in online shopping, capture gaze behavior and through employing machine learning we examine how fast we can accurately predict their ratings. The findings show that after 25 s we can predict ratings with an error rate ranging from 9% to 11% depending on which facet of visual aesthetic is examined. Furthermore, within the first 15 s we can have a good and sufficient prediction for simplicity and colorfulness, with error rates 11% and 12% respectively. For diversity and craftsmanship, 20 s are needed to get a good and sufficient prediction similar to the one from 25 s. The findings indicate that we need more than 10 s of viewing time to be able to accurately capture perceptions on visual aesthetics. The study contributes by offering new ways for designing systems that will take into account users’ gaze behavior in an unobtrusive manner and will be able inform researchers and designers about their perceptions of visual aesthetics.
has issue date	2020-03-10(xsd:dateTime)
bibo:doi	10.1007/978-3-030-45002-1_37
has license	no-cc
sha1sum (hex)	b44ec7c6c7e5104ed5276d72e3309a9f01981f1d
schema:url	https://doi.org/10.1007/978-3-030-45002-1_37
resource representing a document's title	How Quickly Can We Predict Users’ Ratings on Aesthetic Evaluations of Websites? Employing Machine Learning on Eye-Tracking Data
has PubMed Central identifier	PMC7134250
schema:publication	Responsible Design, Implementation and Use of Information and Communication Technology
resource representing a document's body	covid:b44ec7c6c7e5104ed5276d72e3309a9f01981f1d#body_text
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