A comparison of clustering-based privacy-preserving collaborative filtering schemes

BİLGE A., Polat H.

APPLIED SOFT COMPUTING, vol.13, no.5, pp.2478-2489, 2013 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 13 Issue: 5
  • Publication Date: 2013
  • Doi Number: 10.1016/j.asoc.2012.11.046
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.2478-2489
  • Keywords: Privacy, Collaborative filtering, Accuracy, Profiling, Preprocessing, Clustering, RECOMMENDER SYSTEMS, REDUCTION
  • Anadolu University Affiliated: Yes


Privacy-preserving collaborative filtering (PPCF) methods designate extremely beneficial filtering skills without deeply jeopardizing privacy. However, they mostly suffer from scalability, sparsity, and accuracy problems. First, applying privacy measures introduces additional costs making scalability worse. Second, due to randomness for preserving privacy, quality of predictions diminishes. Third, with increasing number of products, sparsity becomes an issue for both CF and PPCF schemes.