Firms implementing digital advertising campaigns face a complex problem in determining the right match between their advertising creatives and target audiences. Typical solutions to the problem have leveraged non-experimental methods, or used "split-testing" strategies that have not explicitly addressed the complexities induced by targeted audiences that can potentially overlap with one another. This paper presents an adaptive algorithm that addresses the problem via online experimentation. The algorithm is set up as a contextual bandit and addresses the overlap issue by partitioning the target audiences into disjoint, non-overlapping sub-populations. It learns an optimal creative display policy in the disjoint space, while assessing in parallel which creative has the best match in the space of possibly overlapping target audiences. Experiments show that the proposed method is more efficient compared to naive "split-testing" or non-adaptive "A/B/n" testing based methods. We also describe a testing product we built that uses the algorithm. The product is currently deployed on the advertising platform of JD.com, an eCommerce company and a publisher of digital ads in China.
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机译：实施数字广告活动的公司面临复杂的问题，用于确定其广告创意与目标受众之间的正确匹配。问题的典型解决方案已经利用了非实验方法，或者使用“分裂测试”策略，这些策略没有明确解决有针对性受众诱导的复杂性，这可能会彼此重叠。本文介绍了一种自适应算法，通过在线实验解决了问题。该算法被设置为上下文强盗，并通过将目标受众划分为不相交的非重叠子群体来解决重叠问题。它在不相交的空间中了解了最佳的创意显示策略，同时并行评估哪些创意在可能重叠目标受众的空间中具有最佳匹配。实验表明，与基于幼稚的“分型测试”或非自适应“A / B / N”的方法相比，该方法更有效。我们还描述了我们构建的测试产品，它使用该算法。该产品目前部署在JD.com的广告平台，电子商务公司和中国数字广告的出版商。
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