Recommendations as Treatments: Debiasing Learning and Evaluation

no code implementations • 17 Feb 2016 • Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims

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.

Causal Inference Recommendation Systems