@inproceedings{schnabel2016recommendations,
author = {Schnabel, Tobias and Swaminathan, Adith and Singh, Ashudeep and Chandak, Navin and Joachims, Thorsten },
title = {Recommendations as Treatments: Debiasing Learning and Evaluation},
booktitle = {2016 International Conference on Machine Learning},
year = {2016},
month = {June},
abstract = {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.},
publisher = {JMLR},
url = {http://approjects.co.za/?big=en-us/research/publication/recommendations-treatments-debiasing-learning-evaluation/},
pages = {1670-1679},
}