论文标题
在延迟反馈下学习分类器，并带有时间窗口假设
Learning Classifiers under Delayed Feedback with a Time Window Assumption
论文作者
论文摘要
我们考虑在延迟反馈（\ emph {df Learning}）下培训二进制分类器。例如，在在线广告中的转换预测中，我们最初收到单击广告但没有购买商品的负样本；随后，其中一些样本购买一个物品，然后更改为正面。在DF学习的环境中，我们会随着时间的推移观察样本，然后在某个时候学习分类器。我们最初收到负样本；随后，其中一些样本变为正变为正。在各种现实世界中，例如在线广告，在首次单击后很长时间进行用户操作，可以想象此问题。由于反馈延迟，正对正和负样本的天真分类返回有偏见的分类器。一种解决方案是，假设这些样品被正确标记，则使用已观察到的样品超过一定的时间窗口。但是，现有研究报告说，仅根据时间窗口假设使用所有样本的子集的性能不佳，并且使用所有样品以及时间窗口假设可以提高经验性能。我们扩展了这些现有的研究，并提出了一种具有公正和凸经验风险的方法，该方法是根据时间窗口假设在所有样本中构成的。为了证明所提出的方法的合理性，我们为在线广告中的真实流量日志数据集提供了实验结果。
We consider training a binary classifier under delayed feedback (\emph{DF learning}). For example, in the conversion prediction in online ads, we initially receive negative samples that clicked the ads but did not buy an item; subsequently, some samples among them buy an item then change to positive. In the setting of DF learning, we observe samples over time, then learn a classifier at some point. We initially receive negative samples; subsequently, some samples among them change to positive. This problem is conceivable in various real-world applications such as online advertisements, where the user action takes place long after the first click. Owing to the delayed feedback, naive classification of the positive and negative samples returns a biased classifier. One solution is to use samples that have been observed for more than a certain time window assuming these samples are correctly labeled. However, existing studies reported that simply using a subset of all samples based on the time window assumption does not perform well, and that using all samples along with the time window assumption improves empirical performance. We extend these existing studies and propose a method with the unbiased and convex empirical risk that is constructed from all samples under the time window assumption. To demonstrate the soundness of the proposed method, we provide experimental results on a synthetic and open dataset that is the real traffic log datasets in online advertising.
