  Recently, recommender systems that aim to suggest personalized lists of items
for users to interact with online have drawn a lot of attention. In fact, many
of these state-of-the-art techniques have been deep learning based. Recent
studies have shown that these deep learning models (in particular for
recommendation systems) are vulnerable to attacks, such as data poisoning,
which generates users to promote a selected set of items. However, more
recently, defense strategies have been developed to detect these generated
users with fake profiles. Thus, advanced injection attacks of creating more
`realistic' user profiles to promote a set of items is still a key challenge in
the domain of deep learning based recommender systems. In this work, we present
our framework CopyAttack, which is a reinforcement learning based black-box
attack method that harnesses real users from a source domain by copying their
profiles into the target domain with the goal of promoting a subset of items.
CopyAttack is constructed to both efficiently and effectively learn policy
gradient networks that first select, and then further refine/craft, user
profiles from the source domain to ultimately copy into the target domain.
CopyAttack's goal is to maximize the hit ratio of the targeted items in the
Top-$k$ recommendation list of the users in the target domain. We have
conducted experiments on two real-world datasets and have empirically verified
the effectiveness of our proposed framework and furthermore performed a
thorough model analysis.
