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Cited by 23Recommender Systems and TechniquesSpam and Phishing DetectionPrivacy-Preserving Technologies in Data

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Targeted Shilling Attacks on GNN-based Recommender Systems

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Guo, Sihan, Bai, Ting, Deng, Weihong·2023·Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
DOI 10.1145/3583780.3615073
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Abstract

GNN-based recommender systems have shown their vulnerability to shilling attacks in recent studies. By conducting shilling attacks on recommender systems, the attackers aim to have homogeneous impacts on all users. However, such indiscriminate attacks suffer from a waste of resources because even if the target item is promoted to users who are not interested, they are unlikely to click on them. In this paper, we conduct targeted shilling attacks in GNN-based recommender systems. By automatically constructing the features and edges of the fake users, our proposed framework AutoAttack achieves accurate attacks on a specific group of users while minimizing the impact on non-target users. Specifically, the features of fake users are generated based on a similarity function, which is optimized according to the features of target users. The structure of fake users is learned by conducting spectral clustering on the target users based on their graph Laplacian matrix, which contains the degree and adjacency information that provides guidance to the edge generation of fake users. We conduct extensive experiments on four real-world datasets in different GNN-based RS and evaluate the performance of our method on the shilling attack and recommendation tasks comprehensively, showing the effectiveness and flexibility of our framework.

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Metadata

Authors
Guo, S., Bai, T., & Deng, W.
Affiliations
Beijing University of Posts and Telecommunications (CN)
ORCID
Venue
Proceedings of the 32nd ACM International Conference on Information and Knowledge Management
Publisher
ACM
Published
2023-10-21
Pages
649-658
ISSN
2155-0751 (ISSN-L)
DOI
10.1145/3583780.3615073
OpenAlex
W4387846639
Cited by
23
References
1
Citations by year
2026: 3 · 2025: 12 · 2024: 8
FWCI
5.92
Type
conference paper
Language
en
Open access
Licence
www.acm.org/publications/policies/copyright_policy#Background (vor)
Locations
ACM International Conference on Information and Knowledge Management (CIKM) (publishedVersion)
Funders
National Natural Science Foundation of China (62102038,62236003)
Topics
  • Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
  • Physical Sciences › Computer Science › Information Systems › Spam and Phishing Detection
  • Physical Sciences › Computer Science › Artificial Intelligence › Privacy-Preserving Technologies in Data
Keywords
shilling attacks, targeted attacks, AutoAttack, spectral clustering, graph Laplacian, user similarity, click-through rate

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