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In online advertising scenario, sellers often create multiple creatives toprovide comprehensive demonstrations, making it essential to present the mostappealing design to maximize the Click-Through Rate (CTR). However, sellersgenerally struggle to consider users preferences for creative design, leadingto the relatively lower aesthetics and quantities compared to ArtificialIntelligence (AI)-based approaches. Traditional AI-based approaches still facethe same problem of not considering user information while having limitedaesthetic knowledge from designers. In fact that fusing the user information,the generated creatives can be more attractive because different users may havedifferent preferences. To optimize the results, the generated creatives intraditional methods are then ranked by another module named creative rankingmodel. The ranking model can predict the CTR score for each creativeconsidering user features. However, the two above stages are regarded as twodifferent tasks and are optimized separately. In this paper, we proposed a newautomated Creative Generation pipeline for Click-Through Rate (CG4CTR) with thegoal of improving CTR during the creative generation stage. Our contributionshave 4 parts: 1) The inpainting mode in stable diffusion is firstly applied tocreative generation task in online advertising scene. A self-cyclic generationpipeline is proposed to ensure the convergence of training. 2) Prompt model isdesigned to generate individualized creatives for different user groups, whichcan further improve the diversity and quality. 3) Reward model comprehensivelyconsiders the multimodal features of image and text to improve theeffectiveness of creative ranking task, and it is also critical in self-cyclicpipeline. 4) The significant benefits obtained in online and offlineexperiments verify the significance of our proposed method.

A New Creative Generation Pipeline for Click-Through Rate with Stable Diffusion Model