  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.
