Predicting Rating Distributions of Website Aesthetics with Deep Learning for AI-Based Research [Vorhersage von Bewertungsverteilungen der Website-Ästhetik mit Deep Learning für die KI-basierte Forschung]

Eisbach, Simon; Daugs, Fabian; Thielsch, Meinald T.; Böhmer, Matthias; Hertel, Guido

Abstract

The aesthetic appeal of a website has strong effects on users’ reactions, appraisals, and even behaviors. However, evaluating website aesthetics through user ratings is resource intensive, and extant models to predict website aesthetics are limited in performance and ability. We contribute a novel and more precise approach to predict website aesthetics that considers rating distributions. Moreover, we use this approach as a baseline model to illustrate how future research might be conducted using predictions instead of participants. Our approach is based on a deep convolutional neural network model and uses innovations in the field of image aesthetic prediction. It was trained with the dataset from Reinecke and Gajos [2014] and was validated using two independent large datasets. The final model reached an unprecedented cross-validated correlation between the ground truth and predicted rating of LCC = 0.752. We then used the model to successfully replicate prior findings and conduct original research as an illustration for AI-based research.