The existing collaborative recommendation algorithms based on matrix factorisation (MF) have poor robustness against shilling attacks. To address this problem, in this study the authors propose a robust collaborative recommendation algorithm based on kernel function and Welsch reweighted M-estimator. They first propose a median-based method to calculate user and item biases, which can reduce the influence of shilling attacks on user and item biases because median is insensitive to outliers. Then, they present a method of similarity computation based on kernel function, which can obtain the information of similar users by non-linear inner product operation. Finally, they combine the user and item biases based on median and the similarity based on kernel function with MF model, and introduce the Welsch reweighted M-estimator to realise the robust estimation of user feature matrix and item feature matrix. The experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing algorithms in terms of both recommendation accuracy and robustness, and the improvement of its robustness is not at the expense of recommendation accuracy.
The existing collaborative recommendation algorithms have poor robustness against shilling attacks. To address this problem, in this paper we propose a robust recommendation method based on suspicious users measurement and multidimensional trust. Firstly, we establish the relevance vector machine classifier according to the user profile features to identify and measure the suspicious users in the user rating database. Secondly, we mine the implicit trust relation among users based on the user-item rating data, and construct a reliable multidimensional trust model by integrating the user suspicion information. Finally, we combine the reliable multidimensional trust model, the neighbor model and matrix factorization model to devise a robust recommendation algorithm. The experimental results on the MovieLens dataset show that the proposed method outperforms the existing methods in terms of both recommendation accuracy and robustness.
Multi-target tracking is widely applied in video surveillance systems. As we know, although the standard particle cardinalized probability hypothesis density filter can estimate state of targets, it is difficult to define the proposal distribution function in prediction stage. Since the robust particles cannot be effectively drawn, the actual tracking accuracy should be enhanced. In this paper, an innovative unscented transform–based particle cardinalized probability hypothesis density filter is derived. Considering the different state spaces, we use the auxiliary particle method and then draw robust particles from the modified distributions in order to estimate the position of targets. Simultaneously, we present the recursion of the optimized Kalman gain to improve the general unscented transform for the velocity estimates. Using the track label, we further integrate them in the framework of the jump Markov model. The simulation results show that the proposed filter has advances in the multi-target tracking scenes. Moreover, the experiments indicate that the filter can track mobile targets with satisfactory results.
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