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

Attack Detection Using Item Vector Shift in Matrix Factorisation Recommenders