Modeling how different user groups perceive webpage aesthetics

Aesthetics is a central consideration in user interface design. It is known to affect end-user behavior and perception, in particular the first impression of graphical user interfaces. However, what users perceive as pleasant or good design is highly subjective. We contribute a computational model that estimates the visual appeal of a given webpage for several common cohorts, or user groups, including gender, age, and education level. Our model, a convolutional neural network trained on 418 webpage screenshots having 771k aesthetic scores (in a 1-9 Likert scale) from 32k users, achieves high accuracy and is always less than 1 point off from ground-truth ratings. Designers can use our model to anticipate how people would rate their webpage, offer personalized designs according to the visual preferences of their users, and support rapid evaluations of webpage design prototypes for specific cohorts.

Automated summary

Aesthetics is a crucial factor in user interface design, however, the perception of pleasant design varies greatly among users. We have developed a computational model that can estimate the visual appeal of a webpage based on different user groups, and it can assist designers in creating personalized designs and evaluating webpage design prototypes quickly.