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 are regarded as two different tasks and are optimized separately. In this paper, we proposed a new automated Creative Generation pipeline for Click-Through Rate (CG4CTR) with the goal of improving CTR during the creative generation stage. Our contributions have 4 parts: 1) The inpainting mode in stable diffusion is firstly applied to creative generation task in online advertising scene. A self-cyclic generation pipeline is proposed to ensure the convergence of training. 2) Prompt model is designed to generate individualized creatives for different user groups, which can further improve the diversity and quality. 3) Reward model comprehensively considers the multimodal features of image and text to improve the effectiveness of creative ranking task, and it is also critical in self-cyclic pipeline. 4) The significant benefits obtained in online and offline experiments verify the significance of our proposed method.

翻译：在线广告场景中，卖家常需制作多种创意素材以提供全面展示，因此必须呈现最具吸引力的设计以最大化点击率（CTR）。然而，卖家通常难以考虑用户对创意设计的偏好，导致其创意在美学和数量上均低于基于人工智能（AI）的方法。传统的AI方法仍存在未考虑用户信息的问题，同时缺乏设计师的美学知识储备。事实上，融合用户信息后生成的创意更具吸引力，因为不同用户可能具有不同偏好。为优化结果，传统方法中生成的创意会经由另一个名为创意排序模型的模块进行排序。该排序模型能基于用户特征预测每个创意的点击率得分。然而，上述两个阶段被视为不同的任务并独立优化。本文提出了一种面向点击率的新型自动化创意生成流程（CG4CTR），旨在创意生成阶段提升点击率。我们的贡献包含四个部分：1）首次将稳定扩散模型的图像修复模式应用于在线广告场景的创意生成任务，并提出自循环生成流程以确保训练收敛性；2）设计提示模型为不同用户群体生成个性化创意，进一步提升了多样性与质量；3）奖励模型综合考量图像与文本的多模态特征以提升创意排序任务的有效性，并在自循环流程中起关键作用；4）线上线下实验取得的显著成效验证了所提方法的重要性。