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Recommender systems are seen as an effective tool to address informationoverload, but it is widely known that the presence of various biases makesdirect training on large-scale observational data result in sub-optimalprediction performance. In contrast, unbiased ratings obtained from randomizedcontrolled trials or A/B tests are considered to be the golden standard, butare costly and small in scale in reality. To exploit both types of data, recentworks proposed to use unbiased ratings to correct the parameters of thepropensity or imputation models trained on the biased dataset. However, theexisting methods fail to obtain accurate predictions in the presence ofunobserved confounding or model misspecification. In this paper, we propose atheoretically guaranteed model-agnostic balancing approach that can be appliedto any existing debiasing method with the aim of combating unobservedconfounding and model misspecification. The proposed approach makes full use ofunbiased data by alternatively correcting model parameters learned with biaseddata, and adaptively learning balance coefficients of biased samples forfurther debiasing. Extensive real-world experiments are conducted along withthe deployment of our proposal on four representative debiasing methods todemonstrate the effectiveness.

Balancing Unobserved Confounding with a Few Unbiased Ratings in Debiased Recommendations