We study offline recommender learning from explicit rating feedback in the
presence of selection bias. A current promising solution for the bias is the
inverse propensity score (IPS) estimation. However, the performance of existing
propensity-based methods can suffer significantly from the propensity
estimation bias. In fact, most of the previous IPS-based methods require some
amount of missing-completely-at-random (MCAR) data to accurately estimate the
propensity. This leads to a critical self-contradiction; IPS is ineffective
without MCAR data, even though it originally aims to learn recommenders from
only missing-not-at-random feedback. To resolve this propensity contradiction,
we derive a propensity-independent generalization error bound and propose a
novel algorithm to minimize the theoretical bound via adversarial learning. Our
theory and algorithm do not require a propensity estimation procedure, thereby
leading to a well-performing rating predictor without the true propensity
information. Extensive experiments demonstrate that the proposed approach is
superior to a range of existing methods both in rating prediction and ranking
metrics in practical settings without MCAR data.

本研究探讨了在选择偏差存在的情况下从明确评分反馈进行离线推荐学习的方法，在推荐偏差的解决方案中，逆偏重评分（IPS）估计是一个当前很有前途的解决方案，但基于倾向性的现有方法的性能会受到倾向性估计偏差的显着影响。因此，我们提出了一种新的算法，该算法通过对抗学习来最小化理论上的偏差，以达到在实际情况中优于一系列现有方法的效果。