论文标题
比较提升：基于强盗的在线广告实验系统
Comparison Lift: Bandit-based Experimentation System for Online Advertising
论文作者
论文摘要
比较升力是在JD.com上测试在线广告受众和创意者的实验-AS服务（EAAS）申请。与许多主要关注固定样品A/B测试的其他EAA工具不同，比较LIFT部署了基于强盗的实验算法。基于匪徒的方法的优点是两个方面。首先，它使测试中引起的随机化与广告客户的测试目标保持一致。其次，通过将实验设计调整为测试期间获得的信息，它大大降低了对广告商的实验成本。自2019年5月推出以来，比较升力已在1,500多个实验中使用。我们估计，该产品的利用有助于将参与广告活动的点击率平均提高46％。我们估计，与固定样品A/B设计相比，产品中的自适应设计平均单击了27％。两者都建议从产品中的广告商节省大量价值和成本。
Comparison Lift is an experimentation-as-a-service (EaaS) application for testing online advertising audiences and creatives at JD.com. Unlike many other EaaS tools that focus primarily on fixed sample A/B testing, Comparison Lift deploys a custom bandit-based experimentation algorithm. The advantages of the bandit-based approach are two-fold. First, it aligns the randomization induced in the test with the advertiser's goals from testing. Second, by adapting experimental design to information acquired during the test, it reduces substantially the cost of experimentation to the advertiser. Since launch in May 2019, Comparison Lift has been utilized in over 1,500 experiments. We estimate that utilization of the product has helped increase click-through rates of participating advertising campaigns by 46% on average. We estimate that the adaptive design in the product has generated 27% more clicks on average during testing compared to a fixed sample A/B design. Both suggest significant value generation and cost savings to advertisers from the product.
