In recommender systems, a common problem is the presence of various biases in the collected data, which deteriorates the generalization ability of the recommendation models and leads to inaccurate predictions. Doubly robust (DR) learning has been studied in many tasks in RS, with the advantage that unbiased learning can be achieved when either a single imputation or a single propensity model is accurate. In this paper, we propose a multiple robust (MR) estimator that can take the advantage of multiple candidate imputation and propensity models to achieve unbiasedness. Specifically, the MR estimator is unbiased when any of the imputation or propensity models, or a linear combination of these models is accurate. Theoretical analysis shows that the proposed MR is an enhanced version of DR when only having a single imputation and propensity model, and has a smaller bias. Inspired by the generalization error bound of MR, we further propose a novel multiple robust learning approach with stabilization. We conduct extensive experiments on real-world and semi-synthetic datasets, which demonstrates the superiority of the proposed approach over state-of-the-art methods.
翻译:在推荐人系统中,一个共同的问题是所收集的数据中存在各种偏差,使建议模型的概括能力恶化,导致不准确的预测。在塞族共和国的许多任务中,已经研究了富有活力的(DR)学习,其优点是,当单一估算或单一偏向模型准确时,可以实现不偏倚的学习。在本文件中,我们提议一个多重稳健(MR)估计器,可以利用多个候选人的估算和倾向模型来实现公正。具体地说,当任何估算或倾向模型或这些模型的线性组合都准确时,MR估计器是不带偏见的。理论分析表明,拟议的MR是只具有单一估算和偏向模型的强化版DR。在通用误差的驱使下,我们进一步提议一种具有稳定性的新颖的多种稳健的学习方法。我们对现实世界和半合成数据集进行了广泛的实验,这表明拟议方法优于状态方法。