Conventional bidding strategies for online display ad auction heavily relies on observed performance indicators such as clicks or conversions. A bidding strategy naively pursuing these easily observable metrics, however, fails to optimize the profitability of the advertisers. Rather, the bidding strategy that leads to the maximum revenue is a strategy pursuing the performance lift of showing ads to a specific user. Therefore, it is essential to predict the lift-effect of showing ads to each user on their target variables from observed log data. However, there is a difficulty in predicting the lift-effect, as the training data gathered by a past bidding strategy may have a strong bias towards the winning impressions. In this study, we develop Unbiased Lift-based Bidding System, which maximizes the advertisers' profit by accurately predicting the lift-effect from biased log data. Our system is the first to enable high-performing lift-based bidding strategy by theoretically alleviating the inherent bias in the log. Real-world, large-scale A/B testing successfully demonstrates the superiority and practicability of the proposed system.

中文翻译：

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基于提升的无偏投标系统

在线展示广告拍卖的传统出价策略在很大程度上依赖于观察到的性能指标，例如点击次数或转化次数。然而，天真地追求这些易于观察的指标的出价策略无法优化广告商的盈利能力。相反，带来最大收入的出价策略是一种追求向特定用户展示广告的性能提升的策略。因此，必须根据观察到的日志数据预测向每个用户展示广告对目标变量的提升效果。然而，预测提升效果存在困难，因为过去出价策略收集的训练数据可能对获胜印象有很强的偏见。在这项研究中，我们开发了基于无偏提升的出价系统，最大限度地提高了广告商的 通过准确预测有偏差的日志数据的提升效应来获利。我们的系统是第一个通过从理论上减轻日志中的固有偏差来启用高性能的基于提升的出价策略的系统。真实世界的大规模 A/B 测试成功证明了所提出系统的优越性和实用性。