PinDCO：大规模整页感知的动态创意优化
PinDCO: Whole-Page Aware Dynamic Creative Optimization at Scale
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中文总结 AI 辅助
PinDCO是Pinterest上用于广告创意检索和选择的生成式动态创意优化系统，通过创意组件融合网络和像素感知调整模块，在十亿级平台上实现整页感知的创意评分与选择，离线分析和在线实验显示广告点击率提升3.09%。
中文摘要 AI 辅助
生成式AI的最新进展极大地加速了高质量广告创意的制作，显著扩大了每个广告活动候选变体的数量。这一转变增加了对可扩展动态创意优化（DCO）系统的需求，该系统能够在严格的延迟和成本约束下，将创意与最相关的受众进行匹配。我们提出了PinDCO，一个用于Pinterest（一个十亿级视觉发现平台）上广告创意检索和选择的生成式DCO系统。PinDCO围绕一个创意组件融合网络（CCFN）构建，该网络通过为每个创意组件（如图像、标题、布局）设置专用塔并使用组件特定的超参数来考虑不同的建模复杂度，从而执行动态创意评分。组件表示被融合以预测基于广告级预测条件的创意级分数，并且我们通过探索-利用策略提高了训练数据质量。为了考虑Pinterest的瀑布流网格布局（其中创意的渲染大小影响附近内容和会话级参与度），我们引入了一个像素感知调整模块（PAM），该模块根据创意大小调整分数，以鼓励高效的屏幕空间利用和更好的整页结果。为了支持大量创意候选，我们进一步采用轻量级预选模型进行早期剪枝，并通过缓存和动态批处理优化服务效率。广泛的离线分析和在线A/B实验证明了PinDCO的有效性，广告点击率（CTR）提升了+3.09%，且整页指标为正。凭借出色的性能，我们已在Pinterest广告平台上线了PinDCO。
英文摘要
Recent advances in generative AI have substantially accelerated the creation of high-quality ad creatives, dramatically expanding the number of candidate variants per campaign. This shift increases the need for scalable dynamic creative optimization (DCO) systems that can match creatives to the most relevant audiences under stringent latency and cost constraints. We present PinDCO, a production DCO system for ad creative retrieval and selection on Pinterest, a billion-scale visual discovery platform. PinDCO is built around a Creative Component Fusion Network (CCFN) that performs dynamic creative scoring by modeling each creative component (e.g., image, title, layout) with a dedicated tower, using component-specific hyperparameters to account for differing modeling complexity. The component representations are fused to predict a creative-level score conditioned on the ad-level prediction, and we improve training data quality via an exploration-exploitation strategy. To account for Pinterest's waterfall grid layout, where a creative's rendered size affects nearby content and session-level engagement, we introduce a Pixel-aware Adjustment Module(PAM) that adjusts scores based on creative size to encourage efficient screen real-estate utilization and better whole-page outcomes. To support the large volume of creative candidates, we further employ a lightweight pre-selection model for early pruning, and optimize serving efficiency through caching and dynamic batching. Extensive offline analyses and online A/B experiments demonstrate the effectiveness of PinDCO, yielding a +3.09% lift in ad Click-Through Rate(CTR) with positive whole-page metrics. With the strong performance, we launched PinDCO in the Pinterest Ads platform.
发表机构
- Pinterest Inc.（Pinterest公司）
机构由 AI 辅助整理，请以论文原文为准。
