—The existing matrix factorization based 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 least median squares estimator. We first propose a method of weight calculation to filter out the largest residuals by introducing the least median squares estimator (LMedS-estimator) of robust statistics, which can reduce the increment of target item’s feature vector caused by shilling attacks. Then we apply the method of weight calculation to RLS-estimator in order to realize the robust estimate of user feature matrix and item feature matrix. Finally, we develop a robust collaborative recommendation algorithm to make predictions. Experimental results on two different-scale MovieLens datasets show that the proposed algorithm outperforms the existing methods in terms of both the prediction accuracy and robustness.
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