A NMF-Based Privacy-Preserving Recommendation Algorithm

The users pay more and more attention to personal information security with the recommender system applied widely. In this paper, a privacy-preserving collaborative filtering algorithm based on non-negative matrix factorization (NMF) is presented, which is combined with random perturbation techniques. The experimental results show that the algorithm cannot only protect users´ privacy, but also generate recommendations with decent accuracy.