To explore the robustness of recommender systems, researchers have proposed various shilling attack models and analyzed their adverse effects. Primitive attacks are highly feasible but less effective due to simplistic handcrafted rules, while upgraded attacks are more powerful but costly and difficult to deploy because they require more knowledge from recommendations. In this paper, we explore a novel shilling attack called Graph cOnvolution-based generative shilling ATtack (GOAT) to balance the attacks' feasibility and effectiveness. GOAT adopts the primitive attacks' paradigm that assigns items for fake users by sampling and the upgraded attacks' paradigm that generates fake ratings by a deep learning-based model. It deploys a generative adversarial network (GAN) that learns the real rating distribution to generate fake ratings. Additionally, the generator combines a tailored graph convolution structure that leverages the correlations between co-rated items to smoothen the fake ratings and enhance their authenticity. The extensive experiments on two public datasets evaluate GOAT's performance from multiple perspectives. Our study of the GOAT demonstrates technical feasibility for building a more powerful and intelligent attack model with a much-reduced cost, enables analysis the threat of such an attack and guides for investigating necessary prevention measures.

中文翻译：

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准备好应对推荐系统的新威胁了吗？基于图卷积的生成先令攻击

为了探索推荐系统的鲁棒性，研究人员提出了各种先令攻击模型并分析了它们的不利影响。原始攻击非常可行但由于简单的手工规则而不太有效，而升级攻击更强大但成本高且难以部署，因为它们需要更多的建议知识。在本文中，我们探索了一种称为基于 Graph cOnvolution 的生成先令攻击（GOAT）的新型先令攻击，以平衡攻击的可行性和有效性。GOAT 采用通过采样为假用户分配物品的原始攻击范式和通过基于深度学习的模型生成假评分的升级攻击范式。它部署了一个生成对抗网络 (GAN)，该网络学习真实评分分布以生成假评分。此外，生成器结合了定制的图卷积结构，该结构利用共同评分项目之间的相关性来平滑虚假评分并增强其真实性。在两个公共数据集上的大量实验从多个角度评估了 GOAT 的性能。我们对 GOAT 的研究证明了以更低的成本构建更强大、更智能的攻击模型的技术可行性，能够分析此类攻击的威胁并指导调查必要的预防措施。