论文标题
反馈转移校正在预测延迟反馈下的转换率时进行校正
A Feedback Shift Correction in Predicting Conversion Rates under Delayed Feedback
论文作者
论文摘要
在展示广告中，预测转换率，即用户在广告商网站上采取预定义动作的可能性，例如购买商品对于估计显示广告的价值至关重要。但是，点击及其结果转换之间存在相对较长的延迟。由于反馈的延迟，在培训期间的一些积极实例被标记为负面，因为收集培训数据时尚未发生某些转换。结果，培训数据和生产环境之间的条件标签分布有所不同。这种情况称为反馈转变。我们通过使用通常用于协变量移位校正的重要性权重方法来解决此问题。我们证明了它在反馈转移方面的一致性。结果进行离线和在线实验表明，我们所提出的方法的表现优于现有方法。
In display advertising, predicting the conversion rate, that is, the probability that a user takes a predefined action on an advertiser's website, such as purchasing goods is fundamental in estimating the value of displaying the advertisement. However, there is a relatively long time delay between a click and its resultant conversion. Because of the delayed feedback, some positive instances at the training period are labeled as negative because some conversions have not yet occurred when training data are gathered. As a result, the conditional label distributions differ between the training data and the production environment. This situation is referred to as a feedback shift. We address this problem by using an importance weight approach typically used for covariate shift correction. We prove its consistency for the feedback shift. Results in both offline and online experiments show that our proposed method outperforms the existing method.
