Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extract...Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.展开更多
针对局部线性嵌入(LLE:Locally Linear Embedding)算法邻域选择不精确及度量方法缺陷导致不能提取流形真实结构的问题,提出一种基于自适应邻域及重构权重的局部线性嵌入算法(AN-RWLLE:Locally Linear Embedding Algorithm Based on Adap...针对局部线性嵌入(LLE:Locally Linear Embedding)算法邻域选择不精确及度量方法缺陷导致不能提取流形真实结构的问题,提出一种基于自适应邻域及重构权重的局部线性嵌入算法(AN-RWLLE:Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight)。首先,通过计算高维样本点的余弦相似性,筛选出每个样本点的局部邻域,再从该邻域中自适应选择最优邻域。其次,融合最优邻域内样本点的距离和结构特征,充分挖掘高维数据流形结构,实现权重重构。最后,利用支持矢量机对特征进行识别,在低维空间保持高维数据的本质特征。实验结果表明,AN-RWLLE算法具有很好的可视化和聚类效果,在两组轴承故障数据集上都具有很好的特征提取能力。展开更多
基金supported by the National Natural Science Foundation of China [Nos. 61772452, 61379116]the Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi [No.2019L0847]the Natural Science Foundation of Hebei Province, China [No. F2015203046]
文摘Faced with the evolving attacks in recommender systems, many detection features have been proposed by human engineering and used in supervised or unsupervised detection methods. However, the detection features extracted by human engineering are usually aimed at some specific types of attacks. To further detect other new types of attacks, the traditional methods have to re-extract detection features with high knowledge cost. To address these limitations, the method for automatic extraction of robust features is proposed and then an Adaboost-based detection method is presented. Firstly, to obtain robust representation with prior knowledge, unlike uniform corruption rate in traditional mLDA(marginalized Linear Denoising Autoencoder), different corruption rates for items are calculated according to the ratings’ distribution. Secondly, the ratings sparsity is used to weight the mapping matrix to extract low-dimensional representation. Moreover, the uniform corruption rate is also set to the next layer in mSLDA(marginalized Stacked Linear Denoising Autoencoder) to extract the stable and robust user features. Finally, under the robust feature space, an Adaboost-based detection method is proposed to alleviate the imbalanced classification problem. Experimental results on the Netflix and Amazon review datasets indicate that the proposed method can effectively detect various attacks.
文摘针对局部线性嵌入(LLE:Locally Linear Embedding)算法邻域选择不精确及度量方法缺陷导致不能提取流形真实结构的问题,提出一种基于自适应邻域及重构权重的局部线性嵌入算法(AN-RWLLE:Locally Linear Embedding Algorithm Based on Adaptive Neighborhood and Reconstruction Weight)。首先,通过计算高维样本点的余弦相似性,筛选出每个样本点的局部邻域,再从该邻域中自适应选择最优邻域。其次,融合最优邻域内样本点的距离和结构特征,充分挖掘高维数据流形结构,实现权重重构。最后,利用支持矢量机对特征进行识别,在低维空间保持高维数据的本质特征。实验结果表明,AN-RWLLE算法具有很好的可视化和聚类效果,在两组轴承故障数据集上都具有很好的特征提取能力。
