Associated with the Linz Institute of Technology and the Institute of Computational Perception @ JKU Linz, Austria

Unlearning Protected User Attributes in Recommendations with Adversarial Training

UnlearningAdversarial Teaser

Abstract

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.


Citation

Christian Ganhör, David Penz, Navid Rekab-saz, Oleg Lesota, Markus Schedl
Unlearning Protected User Attributes in Recommendations with Adversarial Training
Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, doi:10.1145/3477495.3531820, 2022.

BibTeX

@article{Ganhör2022UnlearningAdversarial,
    title = {Unlearning Protected User Attributes in Recommendations with Adversarial Training},
    author = {Ganhör, Christian and Penz, David and Rekab-saz, Navid and Lesota, Oleg and Schedl, Markus},
    journal = {Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval},
    publisher = {Association for Computing Machinery},
    doi = {10.1145/3477495.3531820},
    month = {July},
    year = {2022}
}