This paper proposes a novel method for detecting shilling attacks in Matrix Factorization (MF)-based Recommender Systems (RS), in which attackers use false user-item feedback to promote a specific item. Unlike existing methods that use either use supervised learning to distinguish between attack and genuine profiles or analyse target item rating distributions to detect false ratings, our method uses an unsupervised technique to detect false ratings by examining shifts in item preference vectors that exploit rating deviations and user characteristics, making it a promising new direction. The experimental results demonstrate the effectiveness of our approach in various attack scenarios, including those involving obfuscation techniques.

翻译：本文提出了一种新颖的方法，用于检测基于矩阵分解的推荐系统中的托攻击。在此类攻击中，攻击者利用虚假的用户-物品反馈来推广特定物品。与现有方法不同——现有方法要么使用监督学习来区分攻击用户档案与真实用户档案，要么分析目标物品的评分分布以检测虚假评分——本文方法采用无监督技术，通过考察物品偏好向量中的漂移来检测虚假评分，该技术利用了评分偏差和用户特征，从而开辟了一条有前景的新方向。实验结果表明，我们的方法在各种攻击场景（包括涉及混淆技术的场景）中均具有有效性。