CDR: Conservative Doubly Robust Learning for Debiased Recommendation

TL;DR

This paper introduces CDR, a conservative doubly robust learning method for recommendation systems that filters imputations to reduce bias and improve robustness, addressing issues of poisonous imputation.

Contribution

The paper proposes a novel CDR strategy that filters imputations based on mean and variance, providing theoretical guarantees and improved empirical performance.

Findings

CDR reduces the impact of poisonous imputation.

Theoretical analysis shows reduced variance and better tail bounds.

Experimental results demonstrate significant performance improvements.

Abstract

In recommendation systems (RS), user behavior data is observational rather than experimental, resulting in widespread bias in the data. Consequently, tackling bias has emerged as a major challenge in the field of recommendation systems. Recently, Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties. However, our experimental findings indicate that existing DR methods are severely impacted by the presence of so-called Poisonous Imputation, where the imputation significantly deviates from the truth and becomes counterproductive. To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance. Theoretical analyses show that CDR offers reduced variance and improved tail bounds.In addition, our experimental investigations illustrate that CDR significantly enhances performance and can indeed reduce the frequency of poisonous imputation.