Promoting Recommendations: An Attack on Collaborative Filtering

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How researchers see this paper

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Data Poisoning Attacks on Factorization-Based Collaborative Filtering (2016, background): “L G ] 5 systems and their reliance on user-specified judgments for building profiles, various forms of attacks are possible and have been discussed, such as the random attack and random product push/nuke attack [4, 5].”
Recommender Systems (2016, unknown): “The idea of using fake user profiles for attacking recommendation algorithms was used in [394].”
Preventing shilling attacks in online recommender systems (2005, background): “While there is a lot of work in the field of developing collaborative filtering algorithms, only recently some papers have concentrated on developing shilling attack models [9, 12] and on benchmarking the robustness of recommender systems against shilling attacks [4, 8].”
Shilling recommender systems for fun and profit (2004, methodology): “earlier definition from [15] will be referred to as Power of Attack (POA) and the later definition from [14] will be referred to as Prediction Shift (PredShift).”