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      Computational Model for Webpage Aesthetics using SVM

      proceedings-article

      ,

      Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017) (HCI)

      digital make-believe, with delegates considering our expansive

      3 - 6 July 2017

      Computational Model, Aesthetics Prediction, Support Vector Machine, Empirical Study, Classification, ANOVA

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            Abstract

            Computational model for webpage aesthetics prediction helps designer to determine usability and to improve it. It has been reported that positional geometry of the webpage objects are primarily important for aesthetics computation. In this paper, we propose a computational model for predicting webpage aesthetics based on the positional geometry features of webpage objects. We have considered the best known 13 features that affect aesthetics. By varying these 13 features, we have designed 52 interfaces and rated them by 100 users in a 5 point Likerts scale. Our 1 dimensional ANOVA study on users rating shows, 9 out of the 13 features are important for webpage aesthetics. Based on these 9 features, we created a computational model for webpage aesthetics prediction. Our computational model works based on Support Vector Machine (SVM). To judge the efficacy of our model, we considered 10 popular webpages, and got them rated by 80 users. Experimental results show that our computational model can predict webpage aesthetics with an accuracy of 90%.

            Content

            Author and article information

            Contributors
            Conference
            July 2017
            July 2017
            : 1-5
            Affiliations
            [0001]Department of Computer Science and Engineering Department of Computer Science and Engineering

            Indian Institute of Technology, Guwahati Indian Institute of Technology, Guwahati

            Guwahati - 781039, Assam,India
            Article
            10.14236/ewic/HCI2017.8
            64e3ca12-4276-414d-82c4-f9e8835549b5
            © Maity et al. Published by BCS Learning and Development Ltd. Proceedings of British HCI 2017 – Digital Make-Believe. Sunderland, UK.

            This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

            Proceedings of the 31st International BCS Human Computer Interaction Conference (HCI 2017)
            HCI
            31
            Sunderland, UK
            3 - 6 July 2017
            Electronic Workshops in Computing (eWiC)
            digital make-believe, with delegates considering our expansive
            Product
            Product Information: 1477-9358BCS Learning & Development
            Self URI (journal page): https://ewic.bcs.org/
            Categories
            Electronic Workshops in Computing

            REFERENCES

            1. 2013 Semi-Supervised Learning based Aesthetic Classifier for Short Animations Embedded in Web Pages. INTERACT 2013 728 745

            2. 1995 Support-Vector Networks, Machine Learning, 20 3 273 297

            3. 1997 The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques. John Wiley Sons Inc

            4. 2015 A Non-linear Regression Model to Predict Aesthetics Ratings of On-Screen Images OZCHI 2015 44 52

            5. 2003 Modelling Interface Aesthetics. Information Sciences, 152 25 46

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