基于点击率驱动的广告图像编辑：通过属性解耦与偏好对齐实现创意的迭代优化
Evolving Creatives with Clicks: CTR-Driven Advertising image Editing via Attribute Disentanglement and Preference Alignment
AI总结：
本研究提出ECC方法，通过属性解耦识别与CTR相关的28个属性，结合CPO策略优化广告图像编辑，大幅提升了广告图像的点击率，性能优于现有最先进模型。
摘要：
电商广告创意的优化目标已从审美质量转向点击率（CTR）等在线商业指标。现有方法通过构建基于CTR的奖励信号并利用强化学习优化提示生成，从产品图像生成吸引人的广告图像，但这类范式会同等惩罚表现不佳图像中的所有属性，即便部分属性实际对点击有利。本研究提出Evolving Creatives with Clicks（ECC），将创意优化重新表述为细粒度编辑而非从头生成。ECC选择性优化损害CTR的属性，同时保留对点击有积极贡献的属性。具体而言，面对海量图像的丰富属性空间，ECC首先通过方差分析（ANOVA）和随机森林回归识别出28个与CTR显著相关的属性；基于不同CTR图像间的属性差异，我们对提示模型进行指令微调以生成编辑建议，随后利用真实点击偏好对其进行优化，因为参考生成的建议并非全部有益。此外，我们引入Consistent Preference Optimization（CPO）作为ECC的偏好优化策略，该策略在语义等价的打乱建议间强制保持一致性，以缓解DPO过拟合并提升偏好泛化能力。大量实验表明，ECC显著提升了广告图像的CTR，以大幅优势优于现有最先进模型。完整的数据管道（包括属性提取与分析、生成的数据集及源代码）将在论文接收后发布。
英文摘要：
The optimization objective for e-commerce advertising creatives has shifted from aesthetic quality to online business metrics like click-through rate (CTR). Recent methods generate appealing ad images from product images by constructing CTR-based reward signals and refining prompt generation via Reinforcement Learning. Such paradigms equally penalize all attributes within an underperforming image, even though some are actually beneficial to clicks. In this work, we propose Evolving Creatives with Clicks (ECC), which reformulates creative optimization as fine-grained editing rather than generation from scratch. ECC selectively refines attributes that hurt CTR while preserving those that contribute positively. Specifically, faced with a rich attribute space across massive images, ECC first identifies 28 attributes significantly correlated with CTR via ANOVA and Random Forest regression. Based on attribute contrasts between images with disparate CTRs, we instruction-tune a prompt model to generate editing suggestions, then refine it with real click preferences since reference-derived suggestions are not uniformly beneficial. Furthermore, we introduce Consistent Preference Optimization (CPO) as ECC's preference optimization strategy, which enforces consistency across semantically equivalent shuffled suggestions to mitigate DPO overfitting and improve preference generalization. Extensive experiments demonstrate that ECC significantly improves the CTR of ad images, outperforming state-of-the-art models by a substantial margin. The complete data pipeline, including attribute extraction and analysis, the resulting dataset, and source code will be released upon acceptance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
