Ready for Emerging Threats to Recommender Systems? A Graph Convolution-based Generative Shilling Attack
This work addresses the robustness of recommender systems against emerging threats, providing a more intelligent attack model to guide prevention measures, though it is incremental as it builds on existing paradigms.
The paper tackles the challenge of balancing feasibility and effectiveness in shilling attacks on recommender systems by proposing GOAT, a graph convolution-based generative attack that uses a GAN to generate fake ratings, achieving improved performance with reduced cost compared to existing methods.
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.