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

TL;DR

This paper introduces GOAT, a novel graph convolution-based generative shilling attack that balances attack effectiveness and feasibility, revealing new vulnerabilities in recommender systems and guiding defense strategies.

Contribution

The paper proposes GOAT, a deep learning-based attack model using GANs and graph convolution to generate realistic fake ratings efficiently.

Findings

GOAT effectively mimics real rating distributions.

GOAT demonstrates higher attack success with lower cost.

The study highlights increased vulnerability of recommender systems.

Abstract

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.