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

September 15, 2023 · Entered Twilight · 🏛 Knowledge Discovery and Data Mining

Repo contents: .gitignore, README.md, SOLO, assets, ctr_predictor, datasets, encoder4editing, env, image_embedding, text_embedding

Authors Liyao Jiang, Chenglin Li, Haolan Chen, Xiaodong Gao, Xinwang Zhong, Yang Qiu, Shani Ye, Di Niu arXiv ID 2309.08159 Category cs.CV: Computer Vision Cross-listed cs.IR, cs.LG Citations 0 Venue Knowledge Discovery and Data Mining Repository https://github.com/LiyaoJiang1998/adsee ⭐ 7 Last Checked 1 month ago

Abstract

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.