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
this https URL

本文研究了推荐系统中用户对大多数物品的评级通常是非随机缺失，并提出了三种解决该问题的方法，分别是基于错误插补的方法（EIB）、倒数概率加权方法（IPS）和双重稳健方法（DR）。然而，这些方法忽略了另一种由观察到的评级和用户真实偏好之间不一致性所引起的偏差，即嘈杂反馈或结果测量误差。本研究进一步提出了一种交叉威胁的观点，旨在解决从缺失非随机数据和有噪声数据中学习预测模型的问题，并通过实验证明了所提方法的有效性。