论文标题
公正的基于升降机的招标系统
Unbiased Lift-based Bidding System
论文作者
论文摘要
在线展示广告拍卖的常规招标策略在很大程度上取决于观察到的性能指标，例如点击或转换。然而，竞标策略天真地追求这些易于观察的指标并没有优化广告商的盈利能力。相反，导致最大收入的招标策略是一种策略，追求向特定用户展示广告的性能提升。因此，必须预测从观察到的日志数据中向每个用户显示ADS在目标变量上显示AD的提升效应。但是，很难预测提升效应，因为过去的竞标策略收集的培训数据可能会对获胜的印象有很大的偏见。在这项研究中，我们开发了公正的基于升力的招标系统，从而通过准确预测偏见的日志数据的升力效应来最大化广告商的利润。我们的系统是第一个通过理论上减轻日志中固有的偏见来实现高性能基于升力的招标策略的系统。实际，大规模的A/B测试成功地证明了所提出的系统的优势和实用性。
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.
