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Online advertisements are important elements in e-commerce sites, socialmedia platforms, and search engines. With the increasing popularity of mobilebrowsing, many online ads are displayed with visual information in the form ofa cover image in addition to text descriptions to grab the attention of users.Various recent studies have focused on predicting the click rates of onlineadvertisements aware of visual features or composing optimal advertisementelements to enhance visibility. In this paper, we propose Advertisement StyleEditing and Attractiveness Enhancement (AdSEE), which explores whether semanticediting to ads images can affect or alter the popularity of onlineadvertisements. We introduce StyleGAN-based facial semantic editing andinversion to ads images and train a click rate predictor attributing GAN-basedface latent representations in addition to traditional visual and textualfeatures to click rates. Through a large collected dataset named QQ-AD,containing 20,527 online ads, we perform extensive offline tests to study howdifferent semantic directions and their edit coefficients may impact clickrates. We further design a Genetic Advertisement Editor to efficiently searchfor the optimal edit directions and intensity given an input ad cover image toenhance its projected click rates. Online A/B tests performed over a period of5 days have verified the increased click-through rates of AdSEE-edited samplesas compared to a control group of original ads, verifying the relation betweenimage styles and ad popularity. We open source the code for AdSEE research athttps://github.com/LiyaoJiang1998/adsee.

AdSEE: Investigating the Impact of Image Style Editing on Advertisement Attractiveness