No abstract

Recommender system suffers from huge selection bias from users, which makes utilizing causal inference to solve this problem becomes a necessary problem. However, traditional rating model only utilizing biased dataset and unbiased estimator cannot remarkably be improved. Thus, we fused the unbiased dataset into training model and combine it with jointly learning method to better improve the original baselines. Extensive experiments verify that the proposed doubly robust joint leaning method after fusing unbiased, missing rating data can significantly outperform the state-of-the-art methods and can dramatically reduce the error from rating model with the increasing number of unbiased data.