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Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks

Authors:

Toan Nguyen Thanh,

Nguyen Duc Khang Quach,

Thanh Tam Nguyen,

Thanh Trung Huynh,

Quoc Viet Hung NguyenAuthors Info & Claims

ACM Transactions on Information Systems, Volume 41, Issue 3

Article No.: 58, Pages 1 - 24

https://doi.org/10.1145/3567420

Published: 07 February 2023 Publication History

Abstract

With recent advancements in graph neural networks (GNN), GNN-based recommender systems (gRS) have achieved remarkable success in the past few years. Despite this success, existing research reveals that gRSs are still vulnerable to poison attacks, in which the attackers inject fake data to manipulate recommendation results as they desire. This might be due to the fact that existing poison attacks (and countermeasures) are either model-agnostic or specifically designed for traditional recommender algorithms (e.g., neighborhood-based, matrix-factorization-based, or deep-learning-based RSs) that are not gRS. As gRSs are widely adopted in the industry, the problem of how to design poison attacks for gRSs has become a need for robust user experience. Herein, we focus on the use of poison attacks to manipulate item promotion in gRSs. Compared to standard GNNs, attacking gRSs is more challenging due to the heterogeneity of network structure and the entanglement between users and items. To overcome such challenges, we propose GSPAttack—a generative surrogate-based poison attack framework for gRSs. GSPAttack tailors a learning process to surrogate a recommendation model as well as generate fake users and user-item interactions while preserving the data correlation between users and items for recommendation accuracy. Although maintaining high accuracy for other items rather than the target item seems counterintuitive, it is equally crucial to the success of a poison attack. Extensive evaluations on four real-world datasets revealed that GSPAttack outperforms all baselines with competent recommendation performance and is resistant to various countermeasures.

References

[1]

Himan Abdollahpouri, Masoud Mansoury, Robin Burke, and Bamshad Mobasher. 2019. The unfairness of popularity bias in recommendation. arXiv preprint arXiv:1907.13286 (2019).

[2]

Charu C. Aggarwal et al. 2016. Recommender Systems, Vol. 1. Springer.

[3]

Mehmet Aktukmak, Yasin Yilmaz, and Ismail Uysal. 2019. Quick and accurate attack detection in recommender systems through user attributes. In Proceedings of the 13th ACM Conference on Recommender Systems. 348–352.

Digital Library

[4]

Linas Baltrunas, Karen Church, Alexandros Karatzoglou, and Nuria Oliver. 2015. Frappe: Understanding the usage and perception of mobile app recommendations in-the-wild. arXiv preprint arXiv:1505.03014 (2015).

[5]

Giuseppe Bonaccorso. 2017. Machine Learning Algorithms. Packt Publishing Ltd.

Digital Library

[6]

Paula Branco, Luís Torgo, and Rita P. Ribeiro. 2016. A survey of predictive modeling on imbalanced domains. ACM Comput. Surv. 49, 2 (2016), 1–50.

Digital Library

[7]

BrijainR. Patel and KushikK. Rana. 2014. A survey on decision tree algorithm for classification. J. Eng. Devel. Res. 2, 1 (2014).

[8]

Hongyun Cai, Vincent W. Zheng, and Kevin Chen-Chuan Chang. 2018. A comprehensive survey of graph embedding: Problems, techniques, and applications. IEEE Trans. Knowl. Data Eng. 30, 9 (2018), 1616–1637.

Digital Library

[9]

Taotao Cai, Jianxin Li, Ajmal S. Mian, Timos Sellis, Jeffrey Xu Yu, et al. 2020. Target-aware holistic influence maximization in spatial social networks. IEEE Trans. Knowl. Data Eng. 34, 4 (2020).

[10]

Yi Chang, Zhao Ren, Thanh Tam Nguyen, Wolfgang Nejdl, and Björn W. Schuller. 2022. Example-based explanations with adversarial attacks for respiratory sound analysis. In Proceedings of the Interspeech Conference. 1–5.

[11]

Chong Chen, Min Zhang, Yongfeng Zhang, Yiqun Liu, and Shaoping Ma. 2020. Efficient neural matrix factorization without sampling for recommendation. ACM Trans. Inf. Syst. 38, 2 (2020), 1–28.

Digital Library

[12]

Liang Chen, Yangjun Xu, Fenfang Xie, Min Huang, and Zibin Zheng. 2021. Data poisoning attacks on neighborhood-based recommender systems. Trans. Emerg. Telecommun. Technol. 32, 6 (2021), e3872.

Digital Library

[13]

Tong Chen, Hongzhi Yin, Hongxu Chen, Lin Wu, Hao Wang, Xiaofang Zhou, and Xue Li. 2018. TADA: Trend alignment with dual-attention multi-task recurrent neural networks for sales prediction. In Proceedings of the IEEE International Conference on Data Mining (ICDM). 49–58.

[14]

James Davidson, Benjamin Liebald, Junning Liu, Palash Nandy, Taylor Van Vleet, Ullas Gargi, Sujoy Gupta, Yu He, Mike Lambert, Blake Livingston, et al. 2010. The YouTube video recommendation system. In Proceedings of the 4th ACM Conference on Recommender Systems. 293–296.

Digital Library

[15]

Jingtao Ding, Guanghui Yu, Yong Li, Xiangnan He, and Depeng Jin. 2020. Improving implicit recommender systems with auxiliary data. ACM Trans. Inf. Syst. 38, 1 (2020), 1–27.

Digital Library

[16]

Chi Thang Duong, Thanh Dat Hoang, Ha The Hien Dang, Quoc Viet Hung Nguyen, and Karl Aberer. 2019. On node features for graph neural networks. arXiv preprint arXiv:1911.08795 (2019).

[17]

Hui Fang, Danning Zhang, Yiheng Shu, and Guibing Guo. 2020. Deep learning for sequential recommendation: Algorithms, influential factors, and evaluations. ACM Trans. Inf. Syst. 39, 1 (2020), 1–42.

Digital Library

[18]

Minghong Fang, Guolei Yang, Neil Zhenqiang Gong, and Jia Liu. 2018. Poisoning attacks to graph-based recommender systems. In Proceedings of the 34th Annual Computer Security Applications Conference. 381–392.

Digital Library

[19]

Ian Goodfellow, Yoshua Bengio, and Aaron Courville. 2016. Deep Learning. MIT Press.

Digital Library

[20]

Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio. 2014. Generative adversarial nets. Adv. Neural Inf. Process. Syst. 27 (2014).

[21]

Ihsan Gunes, Cihan Kaleli, Alper Bilge, and Huseyin Polat. 2014. Shilling attacks against recommender systems: A comprehensive survey. Artif. Intell. Rev. 42, 4 (2014), 767–799.

Digital Library

[22]

Lei Guo, Hongzhi Yin, Tong Chen, Xiangliang Zhang, and Kai Zheng. 2021. Hierarchical hyperedge embedding-based representation learning for group recommendation. ACM Trans. Inf. Syst. 40, 1 (2021), 1–27.

Digital Library

[23]

F. Maxwell Harper and Joseph A. Konstan. 2015. The MovieLens datasets: History and context. ACM Trans. Interact. Intell. Syst. 5, 4 (2015), 1–19.

Digital Library

[24]

Ruining He and Julian McAuley. 2016. VBPR: Visual Bayesian personalized ranking from implicit feedback. In Proceedings of the AAAI Conference on Artificial Intelligence.

[25]

Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and powering graph convolution network for recommendation. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 639–648.

Digital Library

[26]

Xiangnan He, Lizi Liao, Hanwang Zhang, Liqiang Nie, Xia Hu, and Tat-Seng Chua. 2017. Neural collaborative filtering. In Proceedings of the 26th International Conference on World Wide Web. 173–182.

Digital Library

[27]

Hai Huang, Jiaming Mu, Neil Zhenqiang Gong, Qi Li, Bin Liu, and Mingwei Xu. 2021. Data poisoning attacks to deep learning based recommender systems. arXiv preprint arXiv:2101.02644 (2021).

[28]

Xiaowen Huang, Jitao Sang, Jian Yu, and Changsheng Xu. 2022. Learning to learn a cold-start sequential recommender. ACM Trans. Inf. Syst. 40, 2 (2022), 1–25.

Digital Library

[29]

Thomas N. Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).

[30]

Wouter Kool, Herke Van Hoof, and Max Welling. 2019. Stochastic beams and where to find them: The Gumbel-Top-k trick for sampling sequences without replacement. In Proceedings of the International Conference on Machine Learning. PMLR, 3499–3508.

[31]

Yehuda Koren, Robert Bell, and Chris Volinsky. 2009. Matrix factorization techniques for recommender systems. Computer 42, 8 (2009), 30–37.

Digital Library

[32]

Bo Li, Yining Wang, Aarti Singh, and Yevgeniy Vorobeychik. 2016. Data poisoning attacks on factorization-based collaborative filtering. Adv. Neural Inf. Process. Syst. 29 (2016).

[33]

Yike Liu, Tara Safavi, Abhilash Dighe, and Danai Koutra. 2018. Graph summarization methods and applications: A survey. ACM Comput. Surv. 51, 3 (2018), 1–34.

Digital Library

[34]

Daniel Lowd and Christopher Meek. 2005. Adversarial learning. In Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining. 641–647.

Digital Library

[35]

Bamshad Mobasher, Robin Burke, Runa Bhaumik, and Chad Williams. 2007. Toward trustworthy recommender systems: An analysis of attack models and algorithm robustness. ACM Trans. Internet Technol. 7, 4 (2007), 23–es.

Digital Library

[36]

Tam Thanh Nguyen, Thanh Trung Huynh, Hongzhi Yin, Vinh Van Tong, Darnbi Sakong, Bolong Zheng, and Quoc Viet Hung Nguyen. 2020. Entity alignment for knowledge graphs with multi-order convolutional networks. IEEE Trans. Knowl. Data Eng. 34, 9 (2020).

[37]

Thanh Toan Nguyen, Thanh Tam Nguyen, Thanh Thi Nguyen, Bay Vo, Jun Jo, and Quoc Viet Hung Nguyen. 2021. JUDO: Just-in-time rumour detection in streaming social platforms. Inf. Sci. 570 (2021), 70–93.

Digital Library

[38]

Thanh Toan Nguyen, Minh Tam Pham, Thanh Tam Nguyen, Thanh Trung Huynh, Quoc Viet Hung Nguyen, Thanh Tho Quan et al. 2021. Structural representation learning for network alignment with self-supervised anchor links. Expert Syst. Applic. 165 (2021), 113857.

[39]

Jianmo Ni, Jiacheng Li, and Julian McAuley. 2019. Justifying recommendations using distantly-labeled reviews and fine-grained aspects. In Proceedings of the Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 188–197.

[40]

Nicolas Papernot, Patrick McDaniel, and Ian Goodfellow. 2016. Transferability in machine learning: From phenomena to black-box attacks using adversarial samples. arXiv preprint arXiv:1605.07277 (2016).

[41]

Ruihong Qiu, Zi Huang, Jingjing Li, and Hongzhi Yin. 2020. Exploiting cross-session information for session-based recommendation with graph neural networks. ACM Trans. Inf. Syst. 38, 3 (2020), 1–23.

Digital Library

[42]

Fatemeh Rezaimehr and Chitra Dadkhah. 2021. A survey of attack detection approaches in collaborative filtering recommender systems. Artif. Intell. Rev. 54, 3 (2021), 2011–2066.

Digital Library

[43]

Badrul Sarwar, George Karypis, Joseph Konstan, and John Riedl. 2001. Item-based collaborative filtering recommendation algorithms. In Proceedings of the 10th International Conference on World Wide Web. 285–295.

Digital Library

[44]

Mingdan Si and Qingshan Li. 2020. Shilling attacks against collaborative recommender systems: A review. Artif. Intell. Rev. 53, 1 (2020), 291–319.

Digital Library

[45]

Junshuai Song, Zhao Li, Zehong Hu, Yucheng Wu, Zhenpeng Li, Jian Li, and Jun Gao. 2020. PoisonRec: An adaptive data poisoning framework for attacking black-box recommender systems. In Proceedings of the IEEE 36th International Conference on Data Engineering (ICDE). IEEE, 157–168.

[46]

Xiangyu Song, Jianxin Li, Qi Lei, Wei Zhao, Yunliang Chen, and Ajmal Mian. 2022. Bi-CLKT: Bi-graph contrastive learning based knowledge tracing. Knowl.-based Syst. 241 (2022), 108274.

Digital Library

[47]

Xiangyu Song, Jianxin Li, Yifu Tang, Taige Zhao, Yunliang Chen, and Ziyu Guan. 2021. JKT: A joint graph convolutional network based deep knowledge tracing. Inf. Sci. 580 (2021), 510–523.

Digital Library

[48]

Agnideven Palanisamy Sundar, Feng Li, Xukai Zou, Tianchong Gao, and Evan D. Russomanno. 2020. Understanding shilling attacks and their detection traits: A comprehensive survey. IEEE Access 8 (2020), 171703–171715.

[49]

Nguyen Thanh Tam, Huynh Thanh Trung, Hongzhi Yin, Tong Van Vinh, Darnbi Sakong, Bolong Zheng, and Nguyen Quoc Viet Hung. 2021. Entity alignment for knowledge graphs with multi-order convolutional networks. In Proceedings of the IEEE 37th International Conference on Data Engineering (ICDE). 2323–2324.

[50]

Nguyen Thanh Tam, Matthias Weidlich, Duong Chi Thang, Hongzhi Yin, and Nguyen Quoc Viet Hung. 2017. Retaining data from streams of social platforms with minimal regret. In Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2850–2856.

Digital Library

[51]

Qiaoyu Tan, Ninghao Liu, Xing Zhao, Hongxia Yang, Jingren Zhou, and Xia Hu. 2020. Learning to hash with graph neural networks for recommender systems. In Proceedings of the Web Conference. 1988–1998.

Digital Library

[52]

Jiaxi Tang, Hongyi Wen, and Ke Wang. 2020. Revisiting adversarially learned injection attacks against recommender systems. In Proceedings of the 14th ACM Conference on Recommender Systems. 318–327.

Digital Library

[53]

Shuchang Tao, Qi Cao, Huawei Shen, Junjie Huang, Yunfan Wu, and Xueqi Cheng. 2021. Single node injection attack against graph neural networks. In Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 1794–1803.

Digital Library

[54]

Florian Tramèr, Fan Zhang, Ari Juels, Michael K. Reiter, and Thomas Ristenpart. 2016. Stealing machine learning models via prediction APIs. In Proceedings of the 25th USENIX Security Symposium (USENIX Security’16). 601–618.

[55]

Huynh Thanh Trung, Tong Van Vinh, Nguyen Thanh Tam, Hongzhi Yin, Matthias Weidlich, and Nguyen Quoc Viet Hung. 2020. Adaptive network alignment with unsupervised and multi-order convolutional networks. In Proceedings of the IEEE 36th International Conference on Data Engineering (ICDE). 85–96.

[56]

Qinyong Wang, Hongzhi Yin, Tong Chen, Junliang Yu, Alexander Zhou, and Xiangliang Zhang. 2022. Fast-adapting and privacy-preserving federated recommender system. VLDB J. 31, 5 (2022), 877–896.

Digital Library

[57]

Xiaoyun Wang, Minhao Cheng, Joe Eaton, Cho-Jui Hsieh, and Felix Wu. 2018. Attack graph convolutional networks by adding fake nodes. arXiv preprint arXiv:1810.10751 (2018).

[58]

Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 165–174.

Digital Library

[59]

Le Wu, Yonghui Yang, Kun Zhang, Richang Hong, Yanjie Fu, and Meng Wang. 2020. Joint item recommendation and attribute inference: An adaptive graph convolutional network approach. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 679–688.

Digital Library

[60]

Shiwen Wu, Fei Sun, Wentao Zhang, and Bin Cui. 2020. Graph neural networks in recommender systems: A survey. arXiv preprint arXiv:2011.02260 (2020).

[61]

Zhiang Wu, Changsheng Li, Jie Cao, and Yong Ge. 2020. On scalability of association-rule-based recommendation: A unified distributed-computing framework. ACM Trans. Web 14, 3 (2020), 1–21.

Digital Library

[62]

Zih-Wun Wu, Chiao-Ting Chen, and Szu-Hao Huang. 2022. Poisoning attacks against knowledge graph-based recommendation systems using deep reinforcement learning. Neural Comput. Applic. 34, 4 (2022), 3097–3115.

Digital Library

[63]

Lianghao Xia, Chao Huang, Yong Xu, Huance Xu, Xiang Li, and Weiguo Zhang. 2021. Collaborative reflection-augmented autoencoder network for recommender systems. ACM Trans. Inf. Syst. 40, 1 (2021), 1–22.

Digital Library

[64]

Feng Xue, Xiangnan He, Xiang Wang, Jiandong Xu, Kai Liu, and Richang Hong. 2019. Deep item-based collaborative filtering for top-N recommendation. ACM Trans. Inf. Syst. 37, 3 (2019), 1–25.

Digital Library

[65]

Jun Yang, Weizhi Ma, Min Zhang, Xin Zhou, Yiqun Liu, and Shaoping Ma. 2021. LegalGNN: Legal information enhanced graph neural network for recommendation. ACM Trans. Inf. Syst. 40, 2 (2021), 1–29.

Digital Library

[66]

Jing Yao, Zhicheng Dou, Jun Xu, and Ji-Rong Wen. 2021. RLPS: A reinforcement learning–based framework for personalized search. ACM Trans. Inf. Syst. 39, 3 (2021), 1–29.

Digital Library

[67]

Feng Yuan, Lina Yao, and Boualem Benatallah. 2019. Adversarial collaborative neural network for robust recommendation. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 1065–1068.

Digital Library

[68]

Zhenrui Yue, Zhankui He, Huimin Zeng, and Julian McAuley. 2021. Black-box attacks on sequential recommenders via data-free model extraction. In Proceedings of the 15th ACM Conference on Recommender Systems. 44–54.

Digital Library

[69]

Hengtong Zhang, Changxin Tian, Yaliang Li, Lu Su, Nan Yang, Wayne Xin Zhao, and Jing Gao. 2021. Data poisoning attack against recommender system using incomplete and perturbed data. In Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. 2154–2164.

Digital Library

[70]

Shuai Zhang, Lina Yao, Aixin Sun, and Yi Tay. 2019. Deep learning based recommender system: A survey and new perspectives. ACM Comput. Surv. 52, 1 (2019), 1–38.

Digital Library

[71]

Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Lizhen Cui, and Xiangliang Zhang. 2021. Graph embedding for recommendation against attribute inference attacks. In Proceedings of the Web Conference. 3002–3014.

Digital Library

[72]

Shijie Zhang, Hongzhi Yin, Tong Chen, Zi Huang, Quoc Viet Hung Nguyen, and Lizhen Cui. 2022. PipAttack: Poisoning federated recommender systems for manipulating item promotion. In Proceedings of the 15th ACM International Conference on Web Search and Data Mining. 1415–1423.

Digital Library

[73]

Shijie Zhang, Hongzhi Yin, Tong Chen, Quoc Viet Nguyen Hung, Zi Huang, and Lizhen Cui. 2020. GCN-based user representation learning for unifying robust recommendation and fraudster detection. In Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval. 689–698.

Digital Library

[74]

Yihe Zhang, Xu Yuan, Jin Li, Jiadong Lou, Li Chen, and Nian-Feng Tzeng. 2021. Reverse attack: Black-box attacks on collaborative recommendation. In Proceedings of the ACM SIGSAC Conference on Computer and Communications Security. 51–68.

Digital Library

[75]

Qi Zhou, Yizhi Ren, Tianyu Xia, Lifeng Yuan, and Linqiang Chen. 2019. Data poisoning attacks on graph convolutional matrix completion. In Proceedings of the International Conference on Algorithms and Architectures for Parallel Processing. Springer, 427–439.

[76]

Dingyuan Zhu, Ziwei Zhang, Peng Cui, and Wenwu Zhu. 2019. Robust graph convolutional networks against adversarial attacks. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 1399–1407.

Digital Library

[77]

Daniel Zügner, Amir Akbarnejad, and Stephan Günnemann. 2018. Adversarial attacks on neural networks for graph data. In Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2847–2856.

Digital Library

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Poisoning GNN-based Recommender Systems with Generative Surrogate-based Attacks

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cover image ACM Transactions on Information Systems

ACM Transactions on Information Systems Volume 41, Issue 3

July 2023

890 pages

ISSN:1046-8188

EISSN:1558-2868

DOI:10.1145/3582880

Editor:
Min Zhang
Tsinghua University, China

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Publication History

Published: 07 February 2023

Online AM: 19 October 2022

Accepted: 04 October 2022

Revised: 25 August 2022

Received: 03 June 2022

Published in TOIS Volume 41, Issue 3

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Sang LZhang YZhang YLi HZhang Y(2025)Towards similar alignment and unique uniformity in collaborative filteringExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.125346259:COnline publication date: 1-Jan-2025
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