Robust Recommendation Algorithm Based on User Rating Matrix Block and Modified LTS-estimator
Yuchen Xu · Journal of Information and Computational Science · 2014
The most widely-used collaborative recommendation algorithms are vulnerable to shilling attacks. To this end, in this paper we propose a robust recommendation algorithm based on user rating matrix block and modified LTS-estimator. Firstly, we construct user rating matrix blocks using user rating matrix block algorithm based on k-median clustering. Secondly, we apply the modified LTS-estimator to matrix factorization model in order to produce user feature matrix and item feature matrix. Finally, we devise a robust recommendation algorithm to generate recommendations for the target users. Experimental results on the MovieLens dataset show that the proposed algorithm outperforms the existing methods in terms of both the prediction accuracy and robustness.