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An interpretable metric of visual aesthetics for GUI design.

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Title: An interpretable metric of visual aesthetics for GUI design.
Authors: Wang, Chen, Miniukovich, Aliaksei, Ren, Xiangshi
Source: Behaviour & Information Technology. Feb2025, Vol. 44 Issue 3, p508-522. 15p.
Subjects: Pearson correlation (Statistics), Graphical user interfaces, Aesthetics, Research funding, Art, Descriptive statistics, Creative ability, Web development, Intraclass correlation, Visual perception, User interfaces, HTML (Document markup language)
Abstract: Computation-based aesthetics metrics have been developed to help designers predict visual aesthetics scores for GUI design. However, designers find these evaluative scores difficult to understand. This paper proposed an interpretable aesthetics metric for GUI design that integrates visual aesthetics (visual similarity and spatial proximity) and GUI structure (semantic similarity and white space) to model visual grouping distribution. Two experiments were conducted to validate the metric's ability to predict aesthetics and interpret outputs. Experiment 1 showed that our metric had a stronger correlation with users' impressions of GUI visual aesthetics than past metrics. Experiment 2 suggested that our metric was easier to interpret and appeared more useful to Visual/Graphic/GUI designers than a conventional score-based alternative, by visualising the metric outputs as an experimental tool. Furthermore, this paper provided five potential insights to further advance computational aesthetics research. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: An interpretable metric of visual aesthetics for GUI design.
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  Data: <searchLink fieldCode="AR" term="%22Wang%2C+Chen%22">Wang, Chen</searchLink><br /><searchLink fieldCode="AR" term="%22Miniukovich%2C+Aliaksei%22">Miniukovich, Aliaksei</searchLink><br /><searchLink fieldCode="AR" term="%22Ren%2C+Xiangshi%22">Ren, Xiangshi</searchLink>
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Feb2025, Vol. 44 Issue 3, p508-522. 15p.
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  Data: <searchLink fieldCode="DE" term="%22Pearson+correlation+%28Statistics%29%22">Pearson correlation (Statistics)</searchLink><br /><searchLink fieldCode="DE" term="%22Graphical+user+interfaces%22">Graphical user interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22Aesthetics%22">Aesthetics</searchLink><br /><searchLink fieldCode="DE" term="%22Research+funding%22">Research funding</searchLink><br /><searchLink fieldCode="DE" term="%22Art%22">Art</searchLink><br /><searchLink fieldCode="DE" term="%22Descriptive+statistics%22">Descriptive statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Creative+ability%22">Creative ability</searchLink><br /><searchLink fieldCode="DE" term="%22Web+development%22">Web development</searchLink><br /><searchLink fieldCode="DE" term="%22Intraclass+correlation%22">Intraclass correlation</searchLink><br /><searchLink fieldCode="DE" term="%22Visual+perception%22">Visual perception</searchLink><br /><searchLink fieldCode="DE" term="%22User+interfaces%22">User interfaces</searchLink><br /><searchLink fieldCode="DE" term="%22HTML+%28Document+markup+language%29%22">HTML (Document markup language)</searchLink>
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  Data: Computation-based aesthetics metrics have been developed to help designers predict visual aesthetics scores for GUI design. However, designers find these evaluative scores difficult to understand. This paper proposed an interpretable aesthetics metric for GUI design that integrates visual aesthetics (visual similarity and spatial proximity) and GUI structure (semantic similarity and white space) to model visual grouping distribution. Two experiments were conducted to validate the metric's ability to predict aesthetics and interpret outputs. Experiment 1 showed that our metric had a stronger correlation with users' impressions of GUI visual aesthetics than past metrics. Experiment 2 suggested that our metric was easier to interpret and appeared more useful to Visual/Graphic/GUI designers than a conventional score-based alternative, by visualising the metric outputs as an experimental tool. Furthermore, this paper provided five potential insights to further advance computational aesthetics research. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=pbh&AN=183029909
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        Value: 10.1080/0144929X.2024.2325030
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        Text: English
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      – SubjectFull: User interfaces
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      – SubjectFull: HTML (Document markup language)
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      – TitleFull: An interpretable metric of visual aesthetics for GUI design.
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            NameFull: Wang, Chen
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              Text: Feb2025
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