With the rapid development of recommender systems in e-commerce industry, such systems bring huge eco-nomic profits. As a consequence, shilling attacks pose a significant threat to the security of collaborative filtering rec-ommender systems. Developing a kind of robust recommendation technology which can resist attacks has become an important issue in the field of the recommender system at present. In this paper, a reputation recommender system is built by user reputations which are obtained from the user historical records. Utilizing the latent factor model in the field of collaborative filtering recommendation, a novel robust collaborative recommendation algorithm based on user reputations is proposed. The algorithm improves the system0s robustness from two aspects of shilling attack and natural noise. Empirical results on Movielens 1M dataset demonstrate that compared with the existing robust recommendation, this algorithm is very effective. Characterized by simplicity, interpretability and stability, the algorithm has strong ability to resist the system attack along with the accuracy getting a certain improvement.
Acta Automatica Sinica