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
Based on relating equation group, a simplified method was presented in terms of the matrix displacement method, which can be conveniently used to study the re-distribution of the internal forces and displacement of tr...Based on relating equation group, a simplified method was presented in terms of the matrix displacement method, which can be conveniently used to study the re-distribution of the internal forces and displacement of truss structures due to the removal of members. Such removal is treated as though adding a load case to the original truss, and the re-distribution can be calculated without modifying the original global stiffness matrix. The computational efficiency of the presented method is faster by square times than that of the matrix displacement method. The results from the two methods are identical.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.
基金Fund of Science and Technology Develop-ment of Shanghai ( No. 0 2 ZF14 0 5 6)
文摘Based on relating equation group, a simplified method was presented in terms of the matrix displacement method, which can be conveniently used to study the re-distribution of the internal forces and displacement of truss structures due to the removal of members. Such removal is treated as though adding a load case to the original truss, and the re-distribution can be calculated without modifying the original global stiffness matrix. The computational efficiency of the presented method is faster by square times than that of the matrix displacement method. The results from the two methods are identical.
