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- W2274664525 abstract "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. To reduce this risk, a number of approaches have been proposed to detect such attacks. Although the existing detection approaches can detect the standard type of these attacks effectively, they perform badly when detecting the recently proposed obfuscated type of these attacks, for example, average over popular items (AoP) attack. With this problem in mind, in this study the author propose a supervised approach to detect such attack. First, he uses the theory of term frequency inverse document frequency (TFIDF) to extract the features of AoP attack. Second, he uses the training set to train support vector machine (SVM) to generate a SVM-based classifier. Finally, he uses the generated classifier to detect the AoP attack. The experimental results on MovieLens dataset show that the proposed approach can detect AoP attack with high recall and precision." @default.
- W2274664525 created "2016-06-24" @default.
- W2274664525 creator A5070345022 @default.
- W2274664525 date "2016-05-01" @default.
- W2274664525 modified "2023-09-30" @default.
- W2274664525 title "Supervised approach for detecting average over popular items attack in collaborative recommender systems" @default.
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- W2274664525 doi "https://doi.org/10.1049/iet-ifs.2015.0067" @default.
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