AdPriRec: a context-aware recommender system for user privacy in MANET services
UIC'11 Proceedings of the 8th international conference on Ubiquitous intelligence and computing
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
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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.