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Personalized recommendation is deemed ubiquitous. Indeed, it has been applied to several online services (e.g., E-commerce, advertising, and social media applications, to name a few). Learning unknown user preferences from user-provided data lies at the core of modern collaborative filtering recommender systems. However, there is an incentive for malicious attackers to manipulate the learned preferences, which could affect business decision making, by injecting poisoned data. In the face of such a poisoning attack, while previous works have proposed a number of defense methods succeeding in other machine learning (ML) tasks, little is effective for collaborative filtering (CF). Thereof, we present a new defense scheme called poison-tolerant collaborative filtering (PTCF), which is highly robust against poisoning attacks on collaborative filtering. Different from the defenses that remove outliers or search a min …