Loss functions for predicted click‐through rates in auctions for online advertising
研究了在线广告拍卖中预测点击率的最优损失函数，发现反映真实经济损失的函数对小错误惩罚大、对大错误惩罚小，模型设定错误时能小幅提升经济效率。
Summary We characterize the optimal loss functions for predicted click‐through rates in auctions for online advertising. Whereas standard loss functions such as mean squared error or log likelihood severely penalize large mispredictions while imposing little penalty on smaller mistakes, a loss function reflecting the true economic loss from mispredictions imposes significant penalties for small mispredictions and only slightly larger penalties on large mispredictions. We illustrate that when the model is misspecified using such a loss function can improve economic efficiency, but the efficiency gain is likely to be small.