CDR: Conservative Doubly Robust Learning for Debiased Recommendation
Zijie Song, Jiawei Chen, Sheng Zhou, Qihao Shi, Yan Feng, Chun Chen, Can Wang
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.
Topics & Concepts
Imputation (statistics)Computer scienceRobustness (evolution)Recommender systemObservational studyMachine learningArtificial intelligenceData miningMissing dataStatisticsMathematicsGeneChemistryBiochemistryRecommender Systems and TechniquesAdvanced Bandit Algorithms ResearchDomain Adaptation and Few-Shot Learning