A robust collaborative recommendation algorithm based on k-distance and Tukey M-estimator

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-distance-based 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.