针对带侧信息的推荐系统，生成能隐藏身份的虚假用户画像进行攻击。
Shilling Recommender Systems by Generating Side-feature-aware Fake User Profiles
- 改进生成器架构，让伪造用户画像匹配真实用户的侧信息特征。
- 在多个基准数据集上实现高攻击成功率且不易被检测到。
- 适合研究推荐系统安全或对抗攻击的学者与工程师参考。
推荐系统极大影响用户消费决策，成为恶意刷单攻击的目标，攻击者通过注入虚假用户画像操纵推荐结果。现有刷单方法在仅使用评分矩阵训练时能生成高效且隐蔽的虚假画像，但缺乏对包含侧信息并利用侧信息的推荐系统的有效应对方案。为此，本文在 Leg-UP 框架基础上，增强生成器架构以融入侧信息，实现侧特征感知的虚假用户画像生成。实验表明，该方法在多个基准数据集上均取得强攻击性能，同时保持良好隐蔽性。
原文摘要 · Abstract (English)
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.
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