In shilling attacks, an adversarial party injects a few fake user profiles into a Recommender System (RS) so that the target item can be promoted or demoted. Although much effort has been devoted to developing shilling attack methods, we find that existing approaches are still far from practical. In this paper, we analyze the properties a practical shilling attack method should have and propose a new concept of Cross-system Attack. With the idea of Cross-system Attack, we design a Practical Cross-system Shilling Attack (PC-Attack) framework that requires little information about the victim RS model and the target RS data for conducting attacks. PC-Attack is trained to capture graph topology knowledge from public RS data in a self-supervised manner. Then, it is fine-tuned on a small portion of target data that is easy to access to construct fake profiles. Extensive experiments have demonstrated the superiority of PC-Attack over state-of-the-art baselines. Our implementation of PC-Attack is available at https://github.com/KDEGroup/PC-Attack.

翻译：在托攻击中，对抗方通过向推荐系统中注入少量虚假用户画像，实现对目标项目的推广或贬抑。尽管已有大量研究致力于托攻击方法的开发，我们发现现有方法仍远未达到实用化需求。本文分析了实用化托攻击方法应具备的性质，并提出“跨系统攻击”这一新概念。基于跨系统攻击思想，我们设计了实用化跨系统托攻击框架（PC-Attack），该框架仅需极少的受害推荐系统模型信息与目标推荐系统数据即可实施攻击。PC-Attack通过自监督学习方式从公开推荐系统数据中捕获图拓扑知识，随后在易于获取的小规模目标数据上进行微调以构建虚假画像。大量实验证明，PC-Attack在性能上显著优于当前最优基准方法。PC-Attack的实现代码已开源于https://github.com/KDEGroup/PC-Attack。