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Recent research has shown the significant vulnerabilities of collaborative recommender systems in the face of profile injection attacks, in which malicious users insert fake profiles into the rating database in order to bias the system's output. A single Support Vector Machine (SVM) approach for the detection of profile injection attacks, however, suffers from low precision. With this problem in mind, in this paper the authors propose a meta-learning-based approach to detect such attacks. In particular, they propose an algorithm to create the diverse base-level training sets through flexible combination of various attack types.
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