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.

翻译：最近,学术界和产业界日益重视偏向性建议模式,现有模式主要基于反偏向性评分技术(IPS),然而,在建议领域,IPS可能很难估计,因为观测到的用户-项目接触数据稀少和杂乱无序。为了缓解这一问题,我们在本文件中假定用户偏好可由少量潜在因素主导,并提议通过增加暴露密度将用户集中起来,以计算更准确的IPS。基本上,这种方法与应用统计中的分级模型精神相似。然而,与以往的超常性分级战略不同,我们通过向用户介绍低级嵌入标准来学习集群标准,今后与推荐模式中的用户代表共享。最后,我们发现,我们的模型与前两类脱偏见推荐人模型有着密切的联系。我们根据真实世界的数据集进行了广泛的实验,以证明拟议方法的有效性。