PoisonRec: An Adaptive Data Poisoning Framework for Attacking Black-box Recommender Systems

Abstract

Data-driven recommender systems that can help to predict users’ preferences are deployed in many real online service platforms. Several studies show that they are vulnerable to data poisoning attacks, and attackers have the ability to mislead the system to perform as their desires. Considering the realistic scenario, where the recommender system is usually a black-box for attackers and complex algorithms may be deployed in them, how to learn effective attack strategies on such recommender systems is still an under-explored problem. In this paper, we propose an adaptive data poisoning framework, PoisonRec, which can automatically learn effective attack strategies on various recommender systems with very limited knowledge. PoisonRec leverages the reinforcement learning architecture, in which an attack agent actively injects fake data (user behaviors) into the recommender system, and then can improve its attack strategies through reward signals that are available under the strict black-box setting. Specifically, we model the attack behavior trajectory as the Markov Decision Process (MDP) in reinforcement learning. We also design a Biased Complete Binary Tree (BCBT) to reformulate the action space for better attack performance. We adopt 8 widely-used representative recommendation algorithms as our testbeds, and make extensive experiments on 4 different real-world datasets. The results show that PoisonRec has the ability to achieve good attack performance on various recommender systems with limited knowledge.