Unlearning Protected User Attributes in Recommendations with Adversarial Training
Open accessAbstract
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.
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Considering the risk of adversarial attacks, adversarial learning can be utilized to detect privacy vulnerabilities within the system, helping to mitigate privacy-related threats.
“Considering the risk of adversarial attacks, adversarial learning can be utilized to detect privacy vulnerabilities within the system, helping to mitigate privacy-related threats [99, 116].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [116]
The presence of bias in the system results in privacy breaches.
“Additionally, the presence of bias in the system results in privacy breaches [116].”
From A Deep Dive into Fairness, Bias, Threats, and Privacy in Recommender Systems: Insights and Future Research (2024) · cites this paper as [116]
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- Authors
- Ganhör, C., Penz, D., Rekabsaz, N., Lesota, O., & Schedl, M.
- Affiliations
- Johannes Kepler University of Linz (AT)
- Venue
- Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
- Publisher
- ACM
- Published
- 2022-07-06
- Pages
- 2142-2147
- OpenAlex
- W4281885833
- Cited by
- 47
- References
- 36
- Citations by year
- 2026: 4 · 2025: 19 · 2024: 11 · 2023: 10 · 2022: 3
- FWCI
- 9.61
- Type
- conference paper
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Austrian Science Fund (FWF) (P33526,DFH-23)
- State of Upper Austria and the Federal Ministry of Education, Science, and Research (LIT-2020-9-SEE-113,LIT-2021-YOU-215)
- Topics
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
- Keywords
- bias mitigation, recommender systems, collaborative filtering, adversarial training, variational autoencoder, gender bias, demographic bias, privacy, adversarial attacks
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