BS-SC: An Unsupervised Approach for Detecting Shilling Profiles in Collaborative Recommender Systems
作者： Hongyun Cai , Fuzhi Zhang
DOI: 10.1109/TKDE.2019.2946247
关键词: Computer science 、 Recommender system 、 Similarity matrix 、 Key (cryptography) 、 Artificial intelligence 、 MovieLens 、 Feature extraction 、 Machine learning 、 Cluster analysis
摘要: Collaborative recommender systems are vulnerable to shilling attacks. To address this issue, many methods including supervised and unsupervised have been proposed. However, supervised detection methods require training classifiers and they only apply to detect known types of attacks. The existing unsupervised detection methods need to know the prior knowledge of attacks, otherwise they suffer from low detection precision. In this paper, we present BS-SC, an unsupervised approach for detecting shilling profiles, which …
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