  Assessing advertisements, specifically on the basis of user preferences and
ad quality, is crucial to the marketing industry. Although recent studies have
attempted to use deep neural networks for this purpose, these studies have not
utilized image-related auxiliary attributes, which include embedded text
frequently found in ad images. We, therefore, investigated the influence of
these attributes on ad image preferences. First, we analyzed large-scale
real-world ad log data and, based on our findings, proposed a novel multi-step
modality fusion network (M2FN) that determines advertising images likely to
appeal to user preferences. Our method utilizes auxiliary attributes through
multiple steps in the network, which include conditional batch
normalization-based low-level fusion and attention-based high-level fusion. We
verified M2FN on the AVA dataset, which is widely used for aesthetic image
assessment, and then demonstrated that M2FN can achieve state-of-the-art
performance in preference prediction using a real-world ad dataset with rich
auxiliary attributes.
