  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.
