Recommender systems (RS) greatly influence users' consumption decisions, making them attractive targets for malicious shilling attacks that inject fake user profiles to manipulate recommendations. Existing shilling methods can generate effective and stealthy fake profiles when training data only contain rating matrix, but they lack comprehensive solutions for scenarios where side features are present and utilized by the recommender. To address this gap, we extend the Leg-UP framework by enhancing the generator architecture to incorporate side features, enabling the generation of side-feature-aware fake user profiles. Experiments on benchmarks show that our method achieves strong attack performance while maintaining stealthiness.

翻译：推荐系统（RS）对用户的消费决策具有重要影响，因此成为恶意注入攻击的目标，攻击者通过注入虚假用户画像以操纵推荐结果。现有的注入方法在训练数据仅包含评分矩阵时能够生成有效且隐蔽的虚假画像，但对于存在侧特征且被推荐系统利用的场景，缺乏全面的解决方案。为填补这一空白，我们扩展了Leg-UP框架，通过增强生成器架构以整合侧特征，从而生成侧特征感知的虚假用户画像。基准测试实验表明，该方法在保持隐蔽性的同时实现了强大的攻击性能。