Attacking Recommender Systems With Plausible Profile

Recommender systems (RS) have become an essential component of web services due to their excellent performance. Despite their great success, RS have proved to be vulnerable to data poisoning attacks, which inject well-crafted fake profiles into RS, so that the target items can be maliciously recommended. In this paper, we first reveal that existing poisoning attacks in RS can be detected effortlessly, as the features of the generated fake profiles cannot be inconsistent with those of normal profiles all the time. We further propose RecUP, a poisoning attack in RS that can generate plausible profiles whose features stay almost the same as the normal ones, based on Generative Adversarial Networks (GAN). To tailor GAN for poisoning in RS, we develop HRGAN and devise a loss function to guide the training of the generator, along with a masking operation with selected potentially powerful profiles, so that the final generated profiles can perform malicious recommendations as expected. Evaluations against various defense methods using three real-world datasets show that, RecUP can generate the most plausible profiles while maintaining comparable attacking performance compared with state-of-the-art attacks.