  Assessing aesthetic preference is a fundamental task related to human
cognition. It can also contribute to various practical applications such as
image creation for online advertisements. Despite crucial influences of image
quality, auxiliary information of ad images such as tags and target subjects
can also determine image preference. Existing studies mainly focus on images
and thus are less useful for advertisement scenarios where rich auxiliary data
are available. Here we propose a modality fusion-based neural network that
evaluates the aesthetic preference of images with auxiliary information. Our
method fully utilizes auxiliary data by introducing multi-step modality fusion
using both conditional batch normalization-based low-level and attention-based
high-level fusion mechanisms, inspired by the findings from statistical
analyses on real advertisement data. Our approach achieved state-of-the-art
performance on the AVA dataset, a widely used dataset for aesthetic assessment.
Besides, the proposed method is evaluated on large-scale real-world
advertisement image data with rich auxiliary attributes, providing promising
preference prediction results. Through extensive experiments, we investigate
how image and auxiliary information together influence click-through rate.
