Debiased Recommendation with Neural Stratification
This work addresses the problem of bias in recommendations for users and platforms, but it is incremental as it builds on existing IPS-based models with a novel clustering approach.
The paper tackles the challenge of estimating inverse propensity scores (IPS) in debiased recommender systems, which is difficult due to sparse and noisy exposure data, by proposing a method that clusters users based on latent factors to compute more accurate IPS, resulting in demonstrated effectiveness on real-world datasets.
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.