发表机构
University of Michigan; Meta（密歇根大学; Meta）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究如何利用历史A/B测试数据，通过性能驱动的离线到在线工作流程，用预测模型指导生成测试广告位候选创意，经实验验证该方法能有效提升创意参与度，为创意优化提供设计原则。
AI 中文摘要
广告创意优化越来越受评估而非生成的限制。生成模型能生成众多看似合理的创意，但可靠评估需在线实验，而每次测试的广告位有限。本文研究如何利用历史A/B测试数据生成并选择测试广告位中的候选创意。我们开发并部署了一个性能驱动的离线到在线工作流程，在离线阶段用基于历史实验训练的预测模型对生成模型创建的变体进行排名和优化，然后在在线自适应实验中部署最终测试广告位。在一个50臂的现场实验中，用此方法生成的最佳创意比最佳人工撰写的创意参与度高45.1%，另外两个实验也呈现相同的上尾模式，提升分别为46.7%和36.2%。结果表明了一种利用生成模型进行创意优化的设计原则。
英文摘要
Ad creative optimization is increasingly constrained by evaluation rather than generation. Generative models can produce many plausible creatives, but reliable evaluation requires online experiments, in which only a limited slate can be tested. We study how to use data from historical A/B tests to generate and select the candidates in that slate. We developed and deployed a performance-driven offline-to-online workflow that guides creative generation with a predictive model as an inference-time critic. In the offline phase, we use a predictive model trained on historical experiments to rank and refine variants created by a generative model. A final test slate is then deployed in an online adaptive experiment. In a 50-arm field experiment, we found that the best creative generated with this method yielded 45.1% higher engagement than the best human-authored creative. Two additional experiments showed the same upper-tail pattern, with lifts of 46.7% and 36.2%. We found that despite the predictive model being too noisy to directly identify the best creative offline, it effectively guides the generative model toward creating strong candidates that can be efficiently evaluated in an adaptive experiment. The results suggest a design principle for creative optimization with generative models: use predictive models to guide generation of a slate to test, judge the slate by whether it contains high-performing candidates at a feasible test size, and use adaptive experiments to select among candidates while limiting traffic lost to weak arms.
