MDI: A Debiasing Method Combining Unbiased and Biased Data

In recent years, many methods have been proposed to alleviate the biases in recommender systems by combining biased data and unbiased data. Among these methods, data imputation method is effective, but previous works only employ a straightforward model to generate imputed data, which can not fully characterize the data. In this paper, we propose a novel data imputation approach that combines an unbiased model and a debiasing model with adaptively learnt weights. We conduct extensive experiments on two public recommendation datasets and one production dataset to demonstrate the effectiveness and robustness of the proposed method.