  Deep learning-based recommender systems have become an integral part of
several online platforms. However, their black-box nature emphasizes the need
for explainable artificial intelligence (XAI) approaches to provide
human-understandable reasons why a specific item gets recommended to a given
user. One such method is counterfactual explanation (CF). While CFs can be
highly beneficial for users and system designers, malicious actors may also
exploit these explanations to undermine the system's security. In this work, we
propose H-CARS, a novel strategy to poison recommender systems via CFs.
Specifically, we first train a logical-reasoning-based surrogate model on
training data derived from counterfactual explanations. By reversing the
learning process of the recommendation model, we thus develop a proficient
greedy algorithm to generate fabricated user profiles and their associated
interaction records for the aforementioned surrogate model. Our experiments,
which employ a well-known CF generation method and are conducted on two
distinct datasets, show that H-CARS yields significant and successful attack
performance.
