Survey on the recommendation attack topic, continuing to update.
[1] Zhang, Shijie, et al. "Pipattack: Poisoning federated recommender systems for manipulating item promotion." Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining. 2022. (对Federated RecSys进行Model Poisoning Attack)
[2] Lin, Chen, et al. "Attacking recommender systems with augmented user profiles." Proceedings of the 29th ACM international conference on information & knowledge management. 2020. (基于GAN对RecSys进行Shilling Attack)
[3] Zhang, Hengtong, et al. "Practical data poisoning attack against next-item recommendation." Proceedings of The Web Conference 2020. (基于RL对RecSys进行Data Poisoning Attack)
[4] Chen, Huiyuan, and Jing Li. "Data poisoning attacks on cross-domain recommendation." Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019. (基于最优化方法对Cross-domain RecSys进行Data Poisoning Attack)
[5] Rong, Dazhong, et al. "FedRecAttack: Model Poisoning Attack to Federated Recommendation." arXiv preprint arXiv:2204.01499 (2022). (对Federated RecSys进行Model Poisoning Attack)
[6] Wu, Chuhan, et al. "FedAttack: Effective and Covert Poisoning Attack on Federated Recommendation via Hard Sampling." arXiv preprint arXiv:2202.04975 (2022). (基于对比学习对Federated RecSys进行Data Poisoning Attack)
[7] Zhang, Xudong, et al. "Targeted Data Poisoning Attack on News Recommendation System." arXiv preprint arXiv:2203.03560 (2022). (基于Reinforcement Learning对New RecSys进行Data Poisoning Attack)
[8] Song, Junshuai, et al. "Poisonrec: an adaptive data poisoning framework for attacking black-box recommender systems." 2020 IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 2020. (基于Reinforcement Learning对RecSys进行Data Poisoning Attack)
[9] Li, Bo, et al. "Data poisoning attacks on factorization-based collaborative filtering." Advances in neural information processing systems 29 (2016). (基于最优化方法对RecSys进行Data Poisoning Attack)
[10] Fan, Wenqi, et al. "Attacking black-box recommendations via copying cross-domain user profiles." 2021 IEEE 37th International Conference on Data Engineering (ICDE). IEEE, 2021. (基于Reinforcement Learning在Cross-domain上进行Data Poisoning Attack)
[11] Liu, Zhuoran, and Martha Larson. "Adversarial item promotion: Vulnerabilities at the core of top-n recommenders that use images to address cold start." Proceedings of the Web Conference 2021. 2021. (对Visually-aware RecSys进行Attack)
[12] Wu, Chenwang, et al. "Fight Fire with Fire: Towards Robust Recommender Systems via Adversarial Poisoning Training." Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021. (通过生成并注入Fake User以提高RecSys Robustness)