  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.
