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- W2372684447 abstract "Research has shown that collaborative technologies recommended vulnerable to attack. While some attack profiles may go undetected at low filler sizes by existence detecting models. In this paper,considered of detection models,we improve the method of Pearson similarity computation method. This method is able to reducing the common evaluation item's impact to compute the user's similarity,and as a result,it will decrease the similarity between the attack users and the real users. The experimental results show that it is effective to raise the systems robust against attack with low filler sizes." @default.
- W2372684447 created "2016-06-24" @default.
- W2372684447 creator A5030216211 @default.
- W2372684447 date "2010-01-01" @default.
- W2372684447 modified "2023-09-25" @default.
- W2372684447 title "An Optimal Method for Detection Model in Collaborative Recommender Systems" @default.
- W2372684447 hasPublicationYear "2010" @default.
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