基于混合策略的反事实学习排序中的位置和信任偏差修正
Mixture-Based Correction for Position and Trust Bias in Counterfactual Learning to Rank
摘要：
在反事实学习排序（CLTR）中，用户交互被用作监督的来源。由于用户交互带有偏见，该领域研究的一个重要焦点在于开发方法来纠正交互的偏见。逆倾向评分法（IPS）是一种适用于纠正位置偏差的流行方法。仿射校正（AC）是IPS的一种推广，它校正了位置偏差和信任偏差。IPS和AC可证明消除偏倚，条件是偏倚参数的准确估计。估计偏置参数，反过来，需要准确估计的相关性概率。这种循环依赖性在灵敏度、收敛性和效率方面引入了实际限制。我们提出了一种新的校正方法，在CLTR中的位置和信任偏差，与现有的方法不同，校正不依赖于相关性估计。我们提出的方法，基于混合的校正（MBC），是基于这样的假设，即在被排名的项目的CTR的分布是两个分布的混合：相关项目的CTR的分布和非相关项目的CTR的分布。我们证明了我们的方法是无偏的。我们的证明的有效性是不以准确的偏置参数估计。我们的实验表明，MBC，当用于不同的偏见设置，并伴随着不同的LTR算法，优于AC，国家的最先进的方法，用于纠正位置和信任偏见，在某些设置中，而在其他设置中表现平平。此外，MBC在训练时间方面比AC更有效。
英文摘要：
In counterfactual learning to rank (CLTR) user interactions are used as a source of supervision. Since user interactions come with bias, an important focus of research in this field lies in developing methods to correct for the bias of interactions. Inverse propensity scoring (IPS) is a popular method suitable for correcting position bias. Affine correction (AC) is a generalization of IPS that corrects for position bias and trust bias. IPS and AC provably remove bias, conditioned on an accurate estimation of the bias parameters. Estimating the bias parameters, in turn, requires an accurate estimation of the relevance probabilities. This cyclic dependency introduces practical limitations in terms of sensitivity, convergence and efficiency. We propose a new correction method for position and trust bias in CLTR in which, unlike the existing methods, the correction does not rely on relevance estimation. Our proposed method, mixture-based correction (MBC), is based on the assumption that the distribution of the CTRs over the items being ranked is a mixture of two distributions: the distribution of CTRs for relevant items and the distribution of CTRs for non-relevant items. We prove that our method is unbiased. The validity of our proof is not conditioned on accurate bias parameter estimation. Our experiments show that MBC, when used in different bias settings and accompanied by different LTR algorithms, outperforms AC, the state-of-the-art method for correcting position and trust bias, in some settings, while performing on par in other settings. Furthermore, MBC is orders of magnitude more efficient than AC in terms of the training time.
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