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.

翻译：在网上广告中,一套潜在的广告可以按某一拍卖系统排列,通常在广告空间选择和展示头一版广告。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,选择偏差破坏了拍卖的真实性,这意味着拍卖中的买主(广告商)不能最大限度地获得利润。虽然在统计领域选择偏差是众所周知的,而且对此进行了大量研究,但我们的主要贡献是把对偏差的理论分析与拍卖机制结合起来。在利用在线A/B测试进行的实验中,我们评估拍卖系统中的选择偏差,其排名是预测的CTR(按速度点击)广告的功能。实验表明,通过多任务学习,学习所有广告的数据,选择偏差会大大减少。