Skip to Main content Skip to Navigation
Conference papers

A Model to Compute Webpage Aesthetics Quality Based on Wireframe Geometry

Abstract : Computational models of web page aesthetics prediction are useful for the designers to determine the usability and to improve it. Positional geometry of webpage objects (wireframe) is an important factor for determining the webpage aesthetics as shown in studies. In this paper, we propose a computational model for predicting webpage aesthetics based on the positional geometry. We have considered 13 features of positional geometry that affect aesthetics, as reported in literature. By varying these 13 features, we have designed 52 interfaces’ wireframes and rated them by 100 users in a 5 point rating 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’ wireframes, and got them rated by 80 users. Experimental results show that our computational model can predict webpage aesthetics with an accuracy of 90%.
Document type :
Conference papers
Complete list of metadatas

Cited literature [14 references]  Display  Hide  Download

https://hal.inria.fr/hal-01717207
Contributor : Hal Ifip <>
Submitted on : Monday, February 26, 2018 - 9:41:02 AM
Last modification on : Saturday, July 21, 2018 - 12:02:21 PM
Long-term archiving on: : Monday, May 28, 2018 - 3:50:19 PM

File

421764_1_En_7_Chapter.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

Citation

Ranjan Maity, Samit Bhattacharya. A Model to Compute Webpage Aesthetics Quality Based on Wireframe Geometry. 16th IFIP Conference on Human-Computer Interaction (INTERACT), Sep 2017, Bombay, India. pp.85-94, ⟨10.1007/978-3-319-67687-6_7⟩. ⟨hal-01717207⟩

Share

Metrics

Record views

424

Files downloads

68