Fairness Attacks on Recommender Systems

• URL: http://arxiv.org/abs/2606.29064v1
• Date: Sat, 27 Jun 2026 19:50:23 GMT
• Title: Fairness Attacks on Recommender Systems
• Abstract summary: We propose a novel structure-aware reinforcement learning-based fairness attack method to exacerbate the unfairness of target recommender systems.<n>Specifically, we first employ a graph-based structure encoder to model the structural dependencies among the generated fake user-item interactions.<n>Based on the learned structure-aware and sequence-aware representations of the fake user and item, the item selection policy attentively decides the next injected fake item.
• Score: 8.484087427925632
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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 interactions. Then, we model the sequential dependency of the injected fake items using a recurrent neural network. Based on the learned structure-aware and sequence-aware representations of the fake user and item, the item selection policy attentively decides the next injected fake item. Since the target recommender system may employ fairness-aware training and leverage the user's sensitive attribute information, such as gender, we further designed a gender selection policy to decide the gender of the entire fake user profile. Both the item selection and gender selection policy are learned jointly in our proposed method. Finally, experimental results on four types of target recommendation models and two real-world datasets demonstrate the effectiveness of the proposed attack method in exacerbating the unfairness of recommender systems.

Related papers

• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Poisoning Federated Recommender Systems with Fake Users [48.70867241987739]
  Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks. We introduce a novel fake user based poisoning attack named PoisonFRS to promote the attacker-chosen targeted item. Experiments on multiple real-world datasets demonstrate that PoisonFRS can effectively promote the attacker-chosen item to a large portion of genuine users.
  arXiv  Detail & Related papers  (2024-02-18T16:34:12Z)
• Model Stealing Attack against Recommender System [85.1927483219819]
  Some adversarial attacks have achieved model stealing attacks against recommender systems. In this paper, we constrain the volume of available target data and queries and utilize auxiliary data, which shares the item set with the target data, to promote model stealing attacks.
  arXiv  Detail & Related papers  (2023-12-18T05:28:02Z)
• Membership Inference Attacks Against Latent Factor Model [0.0]
  We use the latent factor model as the recommender to get the list of recommended items. A shadow recommender is established to derive the labeled training data for the attack model. The experimental data show that the AUC index of our attack model can reach 0.857 on the real dataset MovieLens.
  arXiv  Detail & Related papers  (2022-12-15T08:16:08Z)
• Defending Substitution-Based Profile Pollution Attacks on Sequential Recommenders [8.828396559882954]
  We propose a substitution-based adversarial attack algorithm, which modifies the input sequence by selecting certain vulnerable elements and substituting them with adversarial items. We also design an efficient adversarial defense method called Dirichlet neighborhood sampling. In particular, we represent selected items with one-hot encodings and perform gradient ascent on the encodings to search for the worst case linear combination of item embeddings in training.
  arXiv  Detail & Related papers  (2022-07-19T00:19:13Z)
• Debiasing Learning for Membership Inference Attacks Against Recommender Systems [79.48353547307887]
  Learned recommender systems may inadvertently leak information about their training data, leading to privacy violations. We investigate privacy threats faced by recommender systems through the lens of membership inference. We propose a Debiasing Learning for Membership Inference Attacks against recommender systems (DL-MIA) framework that has four main components.
  arXiv  Detail & Related papers  (2022-06-24T17:57:34Z)
• PipAttack: Poisoning Federated Recommender Systems forManipulating Item Promotion [58.870444954499014]
  A common practice is to subsume recommender systems under the decentralized federated learning paradigm. We present a systematic approach to backdooring federated recommender systems for targeted item promotion.
  arXiv  Detail & Related papers  (2021-10-21T06:48:35Z)
• Membership Inference Attacks Against Recommender Systems [33.66394989281801]
  We make the first attempt on quantifying the privacy leakage of recommender systems through the lens of membership inference. Our attack is on the user-level but not on the data sample-level. A shadow recommender is established to derive the labeled training data for training the attack model.
  arXiv  Detail & Related papers  (2021-09-16T15:19:19Z)
• Data Poisoning Attacks to Deep Learning Based Recommender Systems [26.743631067729677]
  We conduct first systematic study of data poisoning attacks against deep learning based recommender systems. An attacker's goal is to manipulate a recommender system such that the attacker-chosen target items are recommended to many users. To achieve this goal, our attack injects fake users with carefully crafted ratings to a recommender system.
  arXiv  Detail & Related papers  (2021-01-07T17:32:56Z)
• Self-Supervised Reinforcement Learning for Recommender Systems [77.38665506495553]
  We propose self-supervised reinforcement learning for sequential recommendation tasks. Our approach augments standard recommendation models with two output layers: one for self-supervised learning and the other for RL. Based on such an approach, we propose two frameworks namely Self-Supervised Q-learning(SQN) and Self-Supervised Actor-Critic(SAC)
  arXiv  Detail & Related papers  (2020-06-10T11:18:57Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.