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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.

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