In most real-world recommender systems, the observed rating data are subject
to selection bias, and the data are thus missing-not-at-random. Developing a
method to facilitate the learning of a recommender with biased feedback is one
of the most challenging problems, as it is widely known that naive approaches
under selection bias often lead to suboptimal results. A well-established
solution for the problem is using propensity scoring techniques. The propensity
score is the probability of each data being observed, and unbiased performance
estimation is possible by weighting each data by the inverse of its propensity.
However, the performance of the propensity-based unbiased estimation approach
is often affected by choice of the propensity estimation model or the high
variance problem. To overcome these limitations, we propose a model-agnostic
meta-learning method inspired by the asymmetric tri-training framework for
unsupervised domain adaptation. The proposed method utilizes two predictors to
generate data with reliable pseudo-ratings and another predictor to make the
final predictions. In a theoretical analysis, a propensity-independent upper
bound of the true performance metric is derived, and it is demonstrated that
the proposed method can minimize this bound. We conduct comprehensive
experiments using public real-world datasets. The results suggest that the
previous propensity-based methods are largely affected by the choice of
propensity models and the variance problem caused by the inverse propensity
weighting. Moreover, we show that the proposed meta-learning method is robust
to these issues and can facilitate in developing effective recommendations from
biased explicit feedback.

提出了一种元学习方法，受非对称三训练框架的启发，利用两个预测器生成可靠的伪评分数据和另一个预测器进行最终预测，以解决推荐系统中观察到的评分数据选择偏差问题，实现从有偏显式反馈中开发出有效的推荐。