CDR: Conservative Doubly Robust Learning for Debiased Recommendation
- URL: http://arxiv.org/abs/2308.08461v2
- Date: Thu, 17 Aug 2023 05:30:03 GMT
- Title: CDR: Conservative Doubly Robust Learning for Debiased Recommendation
- Authors: ZiJie Song, JiaWei Chen, Sheng Zhou, QiHao Shi, Yan Feng, Chun Chen
and Can Wang
- Abstract summary: Doubly Robust Learning (DR) has gained significant attention due to its remarkable performance and robust properties.
To address this issue, this work proposes Conservative Doubly Robust strategy (CDR) which filters imputations by scrutinizing their mean and variance.
- Score: 23.90593406172408
- License: http://creativecommons.org/licenses/by/4.0/
- 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.
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