Unlearning Protected User Attributes in Recommendations with Adversarial Training

1 code implementation • 9 Jun 2022 • Christian Ganhör, David Penz, Navid Rekabsaz, Oleg Lesota, Markus Schedl

We conduct experiments on the MovieLens-1M and LFM-2b-DemoBias datasets, and evaluate the effectiveness of the bias mitigation method based on the inability of external attackers in revealing the users' gender information from the model.

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