论文标题
校准事项：解决大型广告建议系统中的最大化偏见
Calibration Matters: Tackling Maximization Bias in Large-scale Advertising Recommendation Systems
论文作者
论文摘要
校准定义为平均预测点击率与真实点击率的比率。校准的优化对于许多在线广告推荐系统至关重要，因为它直接影响广告拍卖中的下游竞标和向广告商收取的资金。尽管其重要性，但校准优化通常会遭受一个称为“最大化偏差”的问题。最大化偏置是指预测值的最大值高估真实最大值的现象。引入问题是因为校准是在预测模型本身选择的集合上计算的。即使在每个数据点上都能实现公正的预测，并且当训练集和测试集之间存在协变量时，也会恶化。为了减轻这个问题，我们将最大化偏差的量化理论化，并在本文中提出一个方差调整后的偏差（VAD）元算法。该算法效率，强大且实用，因为它能够减轻协变量转移下的最大偏见问题，既不会产生额外的在线服务成本，也不会损害排名绩效。我们使用最先进的建议神经网络模型在大型现实世界数据集中证明了拟议算法的有效性。
Calibration is defined as the ratio of the average predicted click rate to the true click rate. The optimization of calibration is essential to many online advertising recommendation systems because it directly affects the downstream bids in ads auctions and the amount of money charged to advertisers. Despite its importance, calibration optimization often suffers from a problem called "maximization bias". Maximization bias refers to the phenomenon that the maximum of predicted values overestimates the true maximum. The problem is introduced because the calibration is computed on the set selected by the prediction model itself. It persists even if unbiased predictions can be achieved on every datapoint and worsens when covariate shifts exist between the training and test sets. To mitigate this problem, we theorize the quantification of maximization bias and propose a variance-adjusting debiasing (VAD) meta-algorithm in this paper. The algorithm is efficient, robust, and practical as it is able to mitigate maximization bias problems under covariate shifts, neither incurring additional online serving costs nor compromising the ranking performance. We demonstrate the effectiveness of the proposed algorithm using a state-of-the-art recommendation neural network model on a large-scale real-world dataset.
