A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model
Hao Yang, Jianxin Yuan, Shuai Yang, Linhe Xu, Shuo Yuan, Yifan Zeng
Abstract
In online advertising scenario, sellers often create multiple creatives to provide comprehensive demonstrations, making it essential to present the most appealing design to maximize the Click-Through Rate (CTR). However, sellers generally struggle to consider users' preferences for creative design, leading to the relatively lower aesthetics and quantities compared to Artificial Intelligence (AI)-based approaches. Traditional AI-based approaches still face the same problem of not considering user information while having limited aesthetic knowledge from designers. In fact that fusing the user information, the generated creatives can be more attractive because different users may have different preferences. To optimize the results, the generated creatives in traditional methods are then ranked by another module named creative ranking model. The ranking model can predict the CTR score for each creative considering user features. However, the two above stages (generating creatives and ranking creatives) are regarded as two different tasks and are optimized separately. Specifically, generating creatives in the first stage without considering the target of improving CTR task may generate several creatives with poor quality, leading to dilute online impressions and directly making bad effectiveness on online results.