AdSEE: Investigating the Impact of Image Style Editing on Advertisement
  Attractiveness
Liyao Jiang Chenglin Li Haolan Chen Xiaodong Gao Xinwang Zhong Yang Qiu Shani Ye Di Niu
Online advertisements are important elements in e-commerce sites, social
media platforms, and search engines. With the increasing popularity of mobile
browsing, many online ads are displayed with visual information in the form of
a cover image in addition to text descriptions to grab the attention of users.
Various recent studies have focused on predicting the click rates of online
advertisements aware of visual features or composing optimal advertisement
elements to enhance visibility. In this paper, we propose Advertisement Style
Editing and Attractiveness Enhancement (AdSEE), which explores whether semantic
editing to ads images can affect or alter the popularity of online
advertisements. We introduce StyleGAN-based facial semantic editing and
inversion to ads images and train a click rate predictor attributing GAN-based
face latent representations in addition to traditional visual and textual
features to click rates. Through a large collected dataset named QQ-AD,
containing 20,527 online ads, we perform extensive offline tests to study how
different semantic directions and their edit coefficients may impact click
rates. We further design a Genetic Advertisement Editor to efficiently search
for the optimal edit directions and intensity given an input ad cover image to
enhance its projected click rates. Online A/B tests performed over a period of
5 days have verified the increased click-through rates of AdSEE-edited samples
as compared to a control group of original ads, verifying the relation between
image styles and ad popularity. We open source the code for AdSEE research at
https://github.com/LiyaoJiang1998/adsee.