Recommendations as Treatments: Debiasing Learning and Evaluation
  Most data for evaluating and training recommender systems is subject to selection biases, either through self-selection by the users or through the actions of the recommendation system itself. In this paper, we provide a principled approach to handling selection biases, adapting models and estimation techniques from causal inference. The approach leads to unbiased performance estimators despite biased data, and to a matrix factorization method that provides substantially improved prediction performance on real-world data. We theoretically and empirically characterize the robustness of the approach, finding that it is highly practical and scalable. 
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak and   Thorsten Joachims
cs.LG cs.AI cs.IR