论文标题
在线广告的选择偏差问题的分析
An Analysis of Selection Bias Issue for Online Advertising
论文作者
论文摘要
在在线广告中，可以通过某个拍卖系统对一系列潜在的广告进行排名，其中通常会在广告空间中选择和显示Top-1广告。在本文中，我们展示了拍卖系统中存在的选择偏差问题。我们分析选择偏见破坏了拍卖的真实性，这意味着拍卖中的买家（广告商）无法最大程度地利用他们的利润。尽管选择偏见在统计领域是众所周知的，并且有很多研究，但我们的主要贡献是将偏见的理论分析与拍卖机制相结合。在使用在线A/B测试的实验中，我们评估了拍卖系统上的选择偏差，该拍卖系统的排名得分是预测的CTR（单击速率）广告的函数。该实验表明，通过使用多任务学习来学习所有广告的数据，选择偏差会大大降低。
In online advertising, a set of potential advertisements can be ranked by a certain auction system where usually the top-1 advertisement would be selected and displayed at an advertising space. In this paper, we show a selection bias issue that is present in an auction system. We analyze that the selection bias destroy truthfulness of the auction, which implies that the buyers (advertisers) on the auction can not maximize their profits. Although selection bias is well known in the field of statistics and there are lot of studies for it, our main contribution is to combine the theoretical analysis of the bias with the auction mechanism. In our experiment using online A/B testing, we evaluate the selection bias on an auction system whose ranking score is the function of predicted CTR (click through rate) of advertisement. The experiment showed that the selection bias is drastically reduced by using a multi-task learning which learns the data for all advertisements.
