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， adversaries 向推荐系统注入少量虚假用户档案，以提升或压制目标项目。尽管已有大量研究致力于开发托攻击方法，但我们发现现有方法仍远未具备实用性。本文分析了实用托攻击方法应具备的特性，并提出跨系统攻击这一新概念。基于跨系统攻击理念，我们设计了一种实用的跨系统托攻击框架，该框架仅需极少量关于受害者推荐系统模型和目标推荐系统数据的信息即可实施攻击。该框架通过自监督方式从公开推荐系统数据中捕获图拓扑知识，随后在易于获取的一小部分目标数据上进行微调以构建虚假用户档案。大量实验证明了该框架相较于现有最优基线方法的优越性。我们的实现代码已开源至https://github.com/KDEGroup/PC-Attack。