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

BİLGE A., Polat H.

APPLIED SOFT COMPUTING, cilt.13, sa.5, ss.2478-2489, 2013 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 13 Sayı: 5
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.asoc.2012.11.046
  • Dergi Adı: APPLIED SOFT COMPUTING
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2478-2489
  • Anahtar Kelimeler: Privacy, Collaborative filtering, Accuracy, Profiling, Preprocessing, Clustering, RECOMMENDER SYSTEMS, REDUCTION
  • Anadolu Üniversitesi Adresli: Evet

Özet

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.