The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distanc...The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.展开更多
Parameters k-distance and k-diameter are extension of the distance and the diameter in graph theory. In this paper, the k-distance dk （x,y） between the any vertices x and y is first obtained in a connected circulant...Parameters k-distance and k-diameter are extension of the distance and the diameter in graph theory. In this paper, the k-distance dk （x,y） between the any vertices x and y is first obtained in a connected circulant graph G with order n （n is even） and degree 3 by removing some vertices from the neighbour set of the x. Then, the k-diameters of the connected circulant graphs with order n and degree 3 are given by using the k-diameter dk （x,y）.展开更多
The diameter of a graph G is the maximal distance between pairs of vertices of G. When a network is modeled as a graph,diameter is a measurement for maximum transmission delay. The k-diameter dk(G) of a graph G, which...The diameter of a graph G is the maximal distance between pairs of vertices of G. When a network is modeled as a graph,diameter is a measurement for maximum transmission delay. The k-diameter dk(G) of a graph G, which deals with k internally disjoint paths between pairs of vertices of G, is a extension of the diameter of G. It has widely studied in graph theory and computer science. The circulant graph is a group-theoretic model of a class of symmetric interconnection network. Let Cn(i, n / 2) be a circulant graph of order n whose spanning elements are i and n / 2, where n≥4 and n is even. In this paper, the diameter, 2-diameter and 3-diameter of the Cn(i, n / 2) are all obtained if gcd(n,i)=1, where the symbol gcd(n,i) denotes the maximum common divisor of n and i.展开更多
对于犯罪检测、网络入侵检测等应用,离群点检测是数据挖掘的一种重要算法。局部离群因子是对数据对象离群点的程度定义,计算所有数据对象局部离群因子需要大量计算。一种基于聚类分析局部离群点挖掘改进算法得以实现,此改进算法以聚类...对于犯罪检测、网络入侵检测等应用,离群点检测是数据挖掘的一种重要算法。局部离群因子是对数据对象离群点的程度定义,计算所有数据对象局部离群因子需要大量计算。一种基于聚类分析局部离群点挖掘改进算法得以实现,此改进算法以聚类分析为预处理,只对聚类之外的数据对象计算局部离群因子,避免了大量计算,并改进了对数据对象k距离邻域的求解。通过仿真数据和轨道交通AFC(automatic fare collecting system)客流数据的实验,证实此改进算法不仅能更高效地挖掘出值得关注的离群点,而且还能更好地达到解析目的。展开更多
基金National Natural Science Foundation of China under Grant No.61379116，Natural Science Foundation of Hebei Province under Grant No.F2015203046 and No.F2013203124，Key Program of Research on Science and Technology of Higher Education Institutions of Hebei Province under Grant No.ZH2012028
文摘The existing collaborative recommendation algorithms have lower robustness against shilling attacks.With this problem in mind,in this paper we propose a robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator.Firstly,we propose a k-distancebased method to compute user suspicion degree(USD).The reliable neighbor model can be constructed through incorporating the user suspicion degree into user neighbor model.The influence of attack profiles on the recommendation results is reduced through adjusting similarities among users.Then,Tukey M-estimator is introduced to construct robust matrix factorization model,which can realize the robust estimation of user feature matrix and item feature matrix and reduce the influence of attack profiles on item feature matrix.Finally,a robust collaborative recommendation algorithm is devised by combining the reliable neighbor model and robust matrix factorization model.Experimental results show that the proposed algorithm outperforms the existing methods in terms of both recommendation accuracy and robustness.
文摘Parameters k-distance and k-diameter are extension of the distance and the diameter in graph theory. In this paper, the k-distance dk （x,y） between the any vertices x and y is first obtained in a connected circulant graph G with order n （n is even） and degree 3 by removing some vertices from the neighbour set of the x. Then, the k-diameters of the connected circulant graphs with order n and degree 3 are given by using the k-diameter dk （x,y）.
文摘The diameter of a graph G is the maximal distance between pairs of vertices of G. When a network is modeled as a graph,diameter is a measurement for maximum transmission delay. The k-diameter dk(G) of a graph G, which deals with k internally disjoint paths between pairs of vertices of G, is a extension of the diameter of G. It has widely studied in graph theory and computer science. The circulant graph is a group-theoretic model of a class of symmetric interconnection network. Let Cn(i, n / 2) be a circulant graph of order n whose spanning elements are i and n / 2, where n≥4 and n is even. In this paper, the diameter, 2-diameter and 3-diameter of the Cn(i, n / 2) are all obtained if gcd(n,i)=1, where the symbol gcd(n,i) denotes the maximum common divisor of n and i.
文摘对于犯罪检测、网络入侵检测等应用,离群点检测是数据挖掘的一种重要算法。局部离群因子是对数据对象离群点的程度定义,计算所有数据对象局部离群因子需要大量计算。一种基于聚类分析局部离群点挖掘改进算法得以实现,此改进算法以聚类分析为预处理,只对聚类之外的数据对象计算局部离群因子,避免了大量计算,并改进了对数据对象k距离邻域的求解。通过仿真数据和轨道交通AFC(automatic fare collecting system)客流数据的实验,证实此改进算法不仅能更高效地挖掘出值得关注的离群点,而且还能更好地达到解析目的。
