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Secur."],"published-print":{"date-parts":[[2025,5,31]]},"abstract":"<jats:p>This article proposes a novel method for detecting shilling attacks in Matrix Factorization (MF)\u2013based Recommender Systems (RSs), in which attackers use false user\u2013item feedback to promote a specific item. Unlike existing methods that use either supervised learning to distinguish between attack and genuine profiles or analyze 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. 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