Debiased Recommendation with Neural Stratification

TL;DR

This paper introduces a novel debiased recommendation approach that clusters users based on low-rank embeddings to improve inverse propensity score estimation, leading to more accurate recommendations.

Contribution

It proposes a learned stratification method for IPS estimation in recommender systems, addressing data sparsity and noise issues.

Findings

Improved recommendation accuracy on real-world datasets.

Effective clustering enhances IPS estimation.

Strong theoretical connections to existing debiasing models.

Abstract

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.