Name
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. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ijhcs.2018.11.006 | International Journal of Human-Computer Studies |
Keywords | Field | DocType |
Webpage aesthetics,Deep learning,Web visual design,User experience | Aesthetics,Colorfulness,User experience design,Web page,Computer science,Human–computer interaction,Engineering design process,Artificial intelligence,Deep learning,Artificial neural network | Journal |
Volume | ISSN | Citations |
124 | 1071-5819 | 0 |
PageRank | References | Authors |
0.34 | 29 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qi Dou | 1 | 837 | 57.52 |
| Xianjun Sam Zheng | 2 | 222 | 20.59 |
| Tongfang Sun | 3 | 0 | 0.34 |
| Pheng-Ann Heng | 4 | 3565 | 280.98 |