Ratings of a user to most items in recommender systems are usually missing not at random (MNAR), largely because users are free to choose which items to rate. To achieve unbiased learning of the prediction model under MNAR data, three typical solutions have been proposed, including error-imputation-based (EIB), inverse-propensity-scoring (IPS), and doubly robust (DR) methods. However, these methods ignore an alternative form of bias caused by the inconsistency between the observed ratings and the users' true preferences, also known as noisy feedback or outcome measurement errors (OME), e.g., due to public opinion or low-quality data collection process. In this work, we study intersectional threats to the unbiased learning of the prediction model from data MNAR and OME in the collected data. First, we design OME-EIB, OME-IPS, and OME-DR estimators, which largely extend the existing estimators to combat OME in real-world recommendation scenarios. Next, we theoretically prove the unbiasedness and generalization bound of the proposed estimators. We further propose an alternate denoising training approach to achieve unbiased learning of the prediction model under MNAR data with OME. Extensive experiments are conducted on three real-world datasets and one semi-synthetic dataset to show the effectiveness of our proposed approaches. The code is available at https://github.com/haoxuanli-pku/KDD24-OME-DR.

翻译：推荐系统中用户对大多数物品的评分通常并非随机缺失（MNAR），这主要是由于用户可以自由选择对哪些物品进行评分。为在MNAR数据下实现预测模型的无偏学习，现有研究提出了三类典型解决方案，包括基于误差填补（EIB）、逆倾向评分（IPS）以及双重稳健（DR）方法。然而，这些方法忽略了观测评分与用户真实偏好间不一致性所导致的另一种偏差形式，即噪声反馈或结果测量误差（OME），例如由舆论影响或低质量数据收集过程所引起。本工作研究了从同时存在MNAR与OME的收集数据中对预测模型进行无偏学习时所面临的交叉性挑战。首先，我们设计了OME-EIB、OME-IPS和OME-DR估计器，大幅扩展了现有估计器以应对现实推荐场景中的OME问题。其次，我们从理论上证明了所提估计器的无偏性及其泛化误差界。进一步提出一种交替去噪训练方法，以实现在含OME的MNAR数据下对预测模型的无偏学习。通过在三个真实数据集和一个半合成数据集上的大量实验，验证了所提方法的有效性。代码发布于https://github.com/haoxuanli-pku/KDD24-OME-DR。