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To explore the robustness of recommender systems, researchers have proposedvarious shilling attack models and analyzed their adverse effects. Primitiveattacks are highly feasible but less effective due to simplistic handcraftedrules, while upgraded attacks are more powerful but costly and difficult todeploy because they require more knowledge from recommendations. In this paper,we explore a novel shilling attack called Graph cOnvolution-based generativeshilling ATtack (GOAT) to balance the attacks' feasibility and effectiveness.GOAT adopts the primitive attacks' paradigm that assigns items for fake usersby sampling and the upgraded attacks' paradigm that generates fake ratings by adeep 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 structurethat leverages the correlations between co-rated items to smoothen the fakeratings and enhance their authenticity. The extensive experiments on two publicdatasets evaluate GOAT's performance from multiple perspectives. Our study ofthe GOAT demonstrates technical feasibility for building a more powerful andintelligent attack model with a much-reduced cost, enables analysis the threatof such an attack and guides for investigating necessary prevention measures.

Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack