  Collaborative filtering algorithms capture underlying consumption patterns,
including the ones specific to particular demographics or protected information
of users, e.g. gender, race, and location. These encoded biases can influence
the decision of a recommendation system (RS) towards further separation of the
contents provided to various demographic subgroups, and raise privacy concerns
regarding the disclosure of users' protected attributes. In this work, we
investigate the possibility and challenges of removing specific protected
information of users from the learned interaction representations of a RS
algorithm, while maintaining its effectiveness. Specifically, we incorporate
adversarial training into the state-of-the-art MultVAE architecture, resulting
in a novel model, Adversarial Variational Auto-Encoder with Multinomial
Likelihood (Adv-MultVAE), which aims at removing the implicit information of
protected attributes while preserving recommendation performance. 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. Comparing
with baseline MultVAE, the results show that Adv-MultVAE, with marginal
deterioration in performance (w.r.t. NDCG and recall), largely mitigates
inherent biases in the model on both datasets.
