Fairness Attacks on Recommender Systems
The unfairness of recommender systems has become a topic of concern due to its significant social and ethical implications. Although existing works have shown the effectiveness of attacks on the performance of recommender systems (e.g., promotion and demotion attack), the study of fairness attacks on recommender systems remains largely under-explored. To this end, we propose a novel structure-aware reinforcement learning-based fairness attack method designed to exacerbate the unfairness of target recommender systems. Specifically, we first employ a graph-based structure encoder to model the structural dependencies among the generated fake user-item interactions and the original user-item ...
| Authors | Yanan Wang, Yong Ge |
|---|---|
| Primary category | cs.IR |
| Published | 27 June 2026 |
| Last updated at source | 27 June 2026 |
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