  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.
