  Recent studies have shown that deep neural networks-based recommender systems
are vulnerable to adversarial attacks, where attackers can inject carefully
crafted fake user profiles (i.e., a set of items that fake users have
interacted with) into a target recommender system to achieve malicious
purposes, such as promote or demote a set of target items. Due to the security
and privacy concerns, it is more practical to perform adversarial attacks under
the black-box setting, where the architecture/parameters and training data of
target systems cannot be easily accessed by attackers. However, generating
high-quality fake user profiles under black-box setting is rather challenging
with limited resources to target systems. To address this challenge, in this
work, we introduce a novel strategy by leveraging items' attribute information
(i.e., items' knowledge graph), which can be publicly accessible and provide
rich auxiliary knowledge to enhance the generation of fake user profiles. More
specifically, we propose a knowledge graph-enhanced black-box attacking
framework (KGAttack) to effectively learn attacking policies through deep
reinforcement learning techniques, in which knowledge graph is seamlessly
integrated into hierarchical policy networks to generate fake user profiles for
performing adversarial black-box attacks. Comprehensive experiments on various
real-world datasets demonstrate the effectiveness of the proposed attacking
framework under the black-box setting.
