归因建模提高展示广告竞价的效率
Attribution Modeling Increases Efficiency of Bidding in Display Advertising
摘要：
在广告交易所竞价时预测点击和转化概率是程序化广告行业的核心。以前的两行作品分别解决了i）用户转换概率的预测和ii）这些转换的归因于广告事件（如点击）。我们认为，归因建模提高了效率的投标政策的情况下，性能广告。首先，我们解释了标准投标政策的效率低下的属性。其次，我们学习和利用投标人本身的归因模型，并显示它如何修改点击后的平均出价。最后，我们提出的方法的有效性的证据，在离线和在线实验的数据跨越几个星期的实际流量从Criteo，在性能广告的领导者。
英文摘要：
Predicting click and conversion probabilities when bidding on ad exchanges is at the core of the programmatic advertising industry. Two separated lines of previous works respectively address i) the prediction of user conversion probability and ii) the attribution of these conversions to advertising events (such as clicks) after the fact. We argue that attribution modeling improves the efficiency of the bidding policy in the context of performance advertising. Firstly we explain the inefficiency of the standard bidding policy with respect to attribution. Secondly we learn and utilize an attribution model in the bidder itself and show how it modifies the average bid after a click. Finally we produce evidence of the effectiveness of the proposed method on both offline and online experiments with data spanning several weeks of real traffic from Criteo, a leader in performance advertising.
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