Semi-SAD: applying semi-supervised learning to shilling attack detection
Semi-SAD: applying semi-supervised learning to shilling attack detection
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DOI：
10.1145/2043932.2043985
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发表时间：
2011-10
期刊：
影响因子：
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通讯作者：
Zhiang Wu;Jie Cao;B. Mao;Youquan Wang
中科院分区：
文献类型：
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作者：
Zhiang Wu;Jie Cao;B. Mao;Youquan Wang
基于协同过滤（CF）的推荐系统容易受到先令攻击。在一些领先的电子商务网站中，存在大量的未标记用户，获取其身份的成本很高。现有的先令攻击检测研究未能利用这些未标记的用户。本文提出了一种新的基于半监督学习的先令攻击检测算法Semi-SAD。Semi-SAD使用naïve贝叶斯分类器和EM-»，增强期望最大化（EM）的组合来训练标记和未标记的用户配置文件。在MovieLens数据集上的实验表明，我们提出的半sad是高效的。
Collaborative filtering (CF) based recommender systems are vulnerable to shilling attacks. In some leading e-commerce sites, there exists a large number of unlabeled users, and it is expensive to obtain their identities. Existing research efforts on shilling attack detection fail to exploit these unlabeled users. In this article, Semi-SAD, a new semi-supervised learning based shilling attack detection algorithm is proposed. Semi-SAD is trained with the labeled and unlabeled user profiles using the combination of naïve Bayes classifier and EM-», augmented Expectation Maximization (EM). Experiments on MovieLens datasets show that our proposed Semi-SAD is efficient and effective.