自动分配广告创意配额，提升平台收益6.2%
ACQ: A Deployed Two-Stage Framework for Automated Creative Quota Allocation in Large-Scale Online Advertising
- 两阶段框架：先预测不同配额下的收益，再全局优化分配
- 在线实验显示广告收入提升6.2%，解决创意过多收益递减问题
- 适合大规模广告平台部署，尤其在资源有限时高效决策
在数字广告中，需求方平台（DSP）允许广告主从单张图片生成多个广告创意以参与实时竞价。尽管增加创意数量可提升竞价机会，但其边际收益会递减，且无法无限扩展。这带来一个实际挑战：如何在大规模下自动为每张图片确定合适的创意配额。为此，我们提出自动化创意配额（ACQ）框架，采用两阶段方法。第一阶段使用基于非平衡二叉树的多任务模型，预测不同配额条件下的期望收益，以应对各配额层级间收益分布高度偏斜的问题。第二阶段将配额分配建模为带全局容量约束的多选择背包问题（MCKP），并用高效的基于对偶的算法求解。在快手广告投放平台的离线与在线实验均验证了该框架的有效性，平台广告收入提升了6.20%。
原文摘要 · Abstract (English)
In digital advertising, demand-side platforms (DSPs) allow advertisers to create multiple ad creatives from a single photo for real-time bidding. While increasing the number of creatives can improve bidding opportunities, it cannot scale indefinitely, and the incremental advertising revenue typically exhibits diminishing returns as more creatives are generated. This raises a practical problem for DSPs: how to automatically determine an appropriate creative quota for each photo at scale. To address this problem, we propose Automated Creatives Quota (ACQ), a deployed two-stage framework for creative quota allocation in large-scale online advertising. In the first stage, ACQ predicts quota-conditioned expected revenue using a multi-task model built on an unbalanced binary tree, which is designed to handle the highly skewed revenue distribution across quota levels. In the second stage, ACQ formulates quota allocation under global capacity constraints as a multiple-choice knapsack problem (MCKP) and solves it with an efficient dual-based algorithm. Extensive offline experiments and online experiments on Kuaishou's advertising delivery platform demonstrate the effectiveness of ACQ, achieving a 6.20% increase in platform advertising revenue.
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