Debiased recommender models have recently attracted increasing attention from
the academic and industry communities. Existing models are mostly based on the
technique of inverse propensity score (IPS). However, in the recommendation
domain, IPS can be hard to estimate given the sparse and noisy nature of the
observed user-item exposure data. To alleviate this problem, in this paper, we
assume that the user preference can be dominated by a small amount of latent
factors, and propose to cluster the users for computing more accurate IPS via
increasing the exposure densities. Basically, such method is similar with the
spirit of stratification models in applied statistics. However, unlike previous
heuristic stratification strategy, we learn the cluster criterion by presenting
the users with low ranking embeddings, which are future shared with the user
representations in the recommender model. At last, we find that our model has
strong connections with the previous two types of debiased recommender models.
We conduct extensive experiments based on real-world datasets to demonstrate
the effectiveness of the proposed method.

我们提出了一种针对推荐系统中由于观测到的用户 - 项目曝光数据稀疏和嘈杂导致难以估计 IPS 的问题的方法，该方法通过聚类用户来计算更准确的 IPS，从而提高暴露密度，基于现实世界数据集展开广泛的实验以证明方法的有效性。

神经分层去偏见推荐

Debiased Recommendation with Neural Stratification

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。

解决离线推荐学习中的倾向性矛盾问题

Towards Resolving Propensity Contradiction in Offline Recommender  Learning

There are three quite distinct ways to train a machine learning model on
recommender system logs. The first method is to model the reward prediction for
each possible recommendation to the user, at the scoring time the best
recommendation is found by computing an argmax over the personalized
recommendations. This method obeys principles such as the conditionality
principle and the likelihood principle. A second method is useful when the
model does not fit reality and underfits. In this case, we can use the fact
that we know the distribution of historical recommendations (concentrated on
previously identified good actions with some exploration) to adjust the errors
in the fit to be evenly distributed over all actions. Finally, the inverse
propensity score can be used to produce an estimate of the decision rules
expected performance. The latter two methods violate the conditionality and
likelihood principle but are shown to have good performance in certain
settings. In this paper we review the literature around this fundamental, yet
often overlooked choice and do some experiments using the RecoGym simulation
environment.

本文综述了机器学习在推荐系统日志中的训练方法，并在 RecoGym 仿真环境中进行了实验。三种不同的训练方法分别是模型奖励预测，历史推荐分布调整和倾向性分数逆向估计，并探讨了它们的优缺点和性能表现。