Computational Model for Webpage Aesthetics using SVM

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%.

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Author and article information

Contributors

Ranjan Maity

Samit Bhattacharya

Conference

Publication date: July 2017

Publication date (Print): July 2017

Pages: 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

DOI: 10.14236/ewic/HCI2017.8

SO-VID: 64e3ca12-4276-414d-82c4-f9e8835549b5

License:

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/

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

Conference acronym: HCI

Conference number: 31

Conference location: Sunderland, UK

Conference date: 3 - 6 July 2017

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: digital make-believe, with delegates considering our expansive

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/HCI2017.8

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

D. BansalS. Bhattacharya 2013 Semi-Supervised Learning based Aesthetic Classifier for Short Animations Embedded in Web Pages. INTERACT 2013 728 745
C. CortesV. Vapnik 1995 Support-Vector Networks, Machine Learning, 20 3 273 297
W O. Galitz 1997 The Essential Guide to User Interface Design: An Introduction to GUI Design Principles and Techniques. John Wiley Sons Inc
R. MaityA. UttavG. VermaS. Bhattachariya 2015 A Non-linear Regression Model to Predict Aesthetics Ratings of On-Screen Images OZCHI 2015 44 52
D C L. NgoL S. TeoJ G. Byrne 2003 Modelling Interface Aesthetics. Information Sciences, 152 25 46

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Comment on this article

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

[2] C. CortesV. Vapnik 1995 Support-Vector Networks, Machine Learning, 20 3 273 297

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

[4] R. MaityA. UttavG. VermaS. Bhattachariya 2015 A Non-linear Regression Model to Predict Aesthetics Ratings of On-Screen Images OZCHI 2015 44 52

[5] D C L. NgoL S. TeoJ G. Byrne 2003 Modelling Interface Aesthetics. Information Sciences, 152 25 46

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