  The allure of aesthetic appeal in images captivates our senses, yet the
underlying intricacies of aesthetic preferences remain elusive. In this study,
we pioneer a novel perspective by utilizing several different machine learning
(ML) models that focus on aesthetic attributes known to influence preferences.
Our models process these attributes as inputs to predict the aesthetic scores
of images. Moreover, to delve deeper and obtain interpretable explanations
regarding the factors driving aesthetic preferences, we utilize the popular
Explainable AI (XAI) technique known as SHapley Additive exPlanations (SHAP).
Our methodology compares the performance of various ML models, including Random
Forest, XGBoost, Support Vector Regression, and Multilayer Perceptron, in
accurately predicting aesthetic scores, and consistently observing results in
conjunction with SHAP. We conduct experiments on three image aesthetic
benchmarks, namely Aesthetics with Attributes Database (AADB), Explainable
Visual Aesthetics (EVA), and Personalized image Aesthetics database with Rich
Attributes (PARA), providing insights into the roles of attributes and their
interactions. Finally, our study presents ML models for aesthetics research,
alongside the introduction of XAI. Our aim is to shed light on the complex
nature of aesthetic preferences in images through ML and to provide a deeper
understanding of the attributes that influence aesthetic judgements.
