Recommender systems are seen as an effective tool to address information
overload, but it is widely known that the presence of various biases makes
direct training on large-scale observational data result in sub-optimal
prediction performance. In contrast, unbiased ratings obtained from randomized
controlled trials or A/B tests are considered to be the golden standard, but
are costly and small in scale in reality. To exploit both types of data, recent
works proposed to use unbiased ratings to correct the parameters of the
propensity or imputation models trained on the biased dataset. However, the
existing methods fail to obtain accurate predictions in the presence of
unobserved confounding or model misspecification. In this paper, we propose a
theoretically guaranteed model-agnostic balancing approach that can be applied
to any existing debiasing method with the aim of combating unobserved
confounding and model misspecification. The proposed approach makes full use of
unbiased data by alternatively correcting model parameters learned with biased
data, and adaptively learning balance coefficients of biased samples for
further debiasing. Extensive real-world experiments are conducted along with
the deployment of our proposal on four representative debiasing methods to
demonstrate the effectiveness.

本文提出了一种理论上保证的模型无关均衡方法，该方法可以针对现有的去偏差方法进行应用，以抵抗未观测到的混淆和模型错误，并通过交替校正学习偏差数据的模型参数，以自适应学习平衡系数，充分利用无偏数据。在实际应用中，该方法证明了其有效性。