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。

去偏推荐算法与噪音数据

Debiased Recommendation with Noisy Feedback

We consider the task of identifying and estimating a parameter of interest in
settings where data is missing not at random (MNAR). In general, such
parameters are not identified without strong assumptions on the missing data
model. In this paper, we take an alternative approach and introduce a method
inspired by data fusion, where information in an MNAR dataset is augmented by
information in an auxiliary dataset subject to missingness at random (MAR). We
show that even if the parameter of interest cannot be identified given either
dataset alone, it can be identified given pooled data, under two complementary
sets of assumptions. We derive an inverse probability weighted (IPW) estimator
for identified parameters, and evaluate the performance of our estimation
strategies via simulation studies.

我们考虑的是在数据不随机缺失（MNAR）的情况下识别和估计感兴趣的参数。本文提出了一种受数据融合启发的方法，其中 MNAR 数据集的信息通过与随机缺失（MAR）的辅助数据集的信息来增强。我们展示了在两组互补假设下，即使单独给定任一数据集都无法识别感兴趣的参数，但在汇总数据给定情况下可以识别。我们推导了一种逆概率加权（IPW）估计器用于识别的参数，并通过模拟研究评估了我们的估计策略的性能。

非可忽略缺失数据的识别与估计：一种数据融合方法

Identification and Estimation for Nonignorable Missing Data: A Data  Fusion Approach

Matrix completion is often applied to data with entries missing not at random
(MNAR). For example, consider a recommendation system where users tend to only
reveal ratings for items they like. In this case, a matrix completion method
that relies on entries being revealed at uniformly sampled row and column
indices can yield overly optimistic predictions of unseen user ratings.
Recently, various papers have shown that we can reduce this bias in MNAR matrix
completion if we know the probabilities of different matrix entries being
missing. These probabilities are typically modeled using logistic regression or
naive Bayes, which make strong assumptions and lack guarantees on the accuracy
of the estimated probabilities. In this paper, we suggest a simple approach to
estimating these probabilities that avoids these shortcomings. Our approach
follows from the observation that missingness patterns in real data often
exhibit low nuclear norm structure. We can then estimate the missingness
probabilities by feeding the (always fully-observed) binary matrix specifying
which entries are revealed or missing to an existing nuclear-norm-constrained
matrix completion algorithm by Davenport et al. [2014]. Thus, we tackle MNAR
matrix completion by solving a different matrix completion problem first that
recovers missingness probabilities. We establish finite-sample error bounds for
how accurate these probability estimates are and how well these estimates
debias standard matrix completion losses for the original matrix to be
completed. Our experiments show that the proposed debiasing strategy can
improve a variety of existing matrix completion algorithms, and achieves
downstream matrix completion accuracy at least as good as logistic regression
and naive Bayes debiasing baselines that require additional auxiliary
information.

本文研究了具有缺失非随机性的矩阵补全问题，提出了一种新的缺失概率估计方法，通过观察缺失数据的核范数结构，将缺失数据的概率转化为矩阵补全问题。该方法能够显著降低标准矩阵补全算法在缺失数据情况下预测结果的偏差，实验表明其效果好于传统方法。