Recommendations as Treatments: Debiasing Learning and Evaluation
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 handle selection biases by 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, and find that it is highly practical and scalable.
Cite
Text
Schnabel et al. "Recommendations as Treatments: Debiasing Learning and Evaluation." International Conference on Machine Learning, 2016.Markdown
[Schnabel et al. "Recommendations as Treatments: Debiasing Learning and Evaluation." International Conference on Machine Learning, 2016.](https://mlanthology.org/icml/2016/schnabel2016icml-recommendations/)BibTeX
@inproceedings{schnabel2016icml-recommendations,
title = {{Recommendations as Treatments: Debiasing Learning and Evaluation}},
author = {Schnabel, Tobias and Swaminathan, Adith and Singh, Ashudeep and Chandak, Navin and Joachims, Thorsten},
booktitle = {International Conference on Machine Learning},
year = {2016},
pages = {1670-1679},
volume = {48},
url = {https://mlanthology.org/icml/2016/schnabel2016icml-recommendations/}
}