Assessing classical and expressive aesthetics of web pages using machine learning

• Main Authors: CHEN, Ang, NAH, Fiona Fui-hoon, CHEN, Langtao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2018
• Subjects: Aesthetics; web aesthetics; machine learning; classical aesthetics; expressive aesthetics; overall aesthetics; Artificial Intelligence and Robotics; Databases and Information Systems
• Online Access: https://ink.library.smu.edu.sg/sis_research/10045; https://ink.library.smu.edu.sg/context/sis_research/article/11045/viewcontent/Assessing_Classical_and_Expressive_Aesthetics_of_Web_Pages_pv.pdf
• Institution: Singapore Management University

Aesthetics plays a key role in web design. However, most websites are developed based on designers’ "inspirations" or "educated guesses" (Liu, 2003). While perceptions of aesthetics are intuitive abilities of humankind, the underlying principles for assessing aesthetics are not well understood. In this research, we propose using machine learning techniques to explore and more fully understand the patterns and underlying principles of aesthetics. We propose using machine learning techniques to develop predictive models for two aesthetic dimensions – classical aesthetics and expressive aesthetics – as well as for overall aesthetics of web pages in order to evaluate the aesthetic quality of web pages.