Webthetics: Quantifying webpage aesthetics with deep learning. Issue 124 (April 2019)

• Record Type: Journal Article
• Authors: Dou, Qi; Zheng, Xianjun Sam; Sun, Tongfang; Heng, Pheng-Ann
• Is Part Of: International journal of human-computer studies. Issue 124(2019)
• Page Start: 56
• Page End: 66
• Publication Date: 2019-04
• Subjects: Webpage aesthetics -- Deep learning -- Web visual design -- User experience; Human-machine systems -- Periodicals; Systems engineering -- Periodicals; Human engineering -- Periodicals; Human engineering; Human-machine systems; Systems engineering; Periodicals; Electronic journals; 004.019
• DOI: 10.1016/j.ijhcs.2018.11.006
• Languages: English
• ISSNs: 1071-5819

Abstract

Abstract: As web has become the most popular media to attract users and customers worldwide, webpage aesthetics plays an increasingly important role for engaging users online and impacting their user experience. We present a novel method using deep learning to automatically compute and quantify webpage aesthetics. Our deep neural network, named as Webthetics, which is trained from the collected user rating data, can extract representative features from raw webpages and quantify their aesthetics. To improve the model performance, we propose to transfer the knowledge from image style recognition task into our network. We have validated that our method significantly outperforms previous method using hand-crafted features such as colorfulness and complexity. These promising results indicate that our method can serve as an effective and efficient means for providing objective aesthetics evaluation during the design process.