Recent studies have shown that recommender systems (RSs) are highly
vulnerable to data poisoning attacks. Understanding attack tactics helps
improve the robustness of RSs. We intend to develop efficient attack methods
that use limited resources to generate high-quality fake user profiles to
achieve 1) transferability among black-box RSs 2) and imperceptibility among
detectors. In order to achieve these goals, we introduce textual reviews of
products to enhance the generation quality of the profiles. Specifically, we
propose a novel attack framework named R-Trojan, which formulates the attack
objectives as an optimization problem and adopts a tailored transformer-based
generative adversarial network (GAN) to solve it so that high-quality attack
profiles can be produced. Comprehensive experiments on real-world datasets
demonstrate that R-Trojan greatly outperforms state-of-the-art attack methods
on various victim RSs under black-box settings and show its good
imperceptibility.

通过引入产品的文本评论以提高生成质量，我们提出了一种名为 R-Trojan 的新型攻击框架，将攻击目标表述为优化问题，并采用定制的基于转换器的生成对抗网络 (GAN) 进行求解，从而产生高质量的攻击性用户配置文件。在黑盒设置下对各个受害者推荐系统进行的广泛实验证明了 R-Trojan 在多个受害者推荐系统上明显优于现有最先进的攻击方法，并展示了其良好的不可察觉性。