  Consumers are exposed to advertisements across many different domains on the
internet, such as fashion, beauty, car, food, and others. On the other hand,
fashion represents second highest e-commerce shopping category. Does consumer
digital record behavior on various fashion ad images reveal their fashion
taste? Does ads from other domains infer their fashion taste as well? In this
paper, we study the correlation between advertisements and fashion taste.
Towards this goal, we introduce a new dataset, Fashionpedia-Ads, which asks
subjects to provide their preferences on both ad (fashion, beauty, car, and
dessert) and fashion product (social network and e-commerce style) images.
Furthermore, we exhaustively collect and annotate the emotional, visual and
textual information on the ad images from multi-perspectives (abstractive
level, physical level, captions, and brands). We open-source Fashionpedia-Ads
to enable future studies and encourage more approaches to interpretability
research between advertisements and fashion taste.
