Estimating NBC-based recommendations on arbitrarily partitioned data with privacy

Yakut I., Polat H.

KNOWLEDGE-BASED SYSTEMS, cilt.36, ss.353-362, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 36
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.knosys.2012.07.015
  • Dergi Adı: KNOWLEDGE-BASED SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.353-362
  • Anahtar Kelimeler: Privacy, Arbitrary partitioning, Binary recommendation, Naive Bayesian classifier, Sparsity, ASSOCIATION RULES
  • Anadolu Üniversitesi Adresli: Evet

Özet

Providing partitioned data-based recommendations has been receiving increasing attention due to mutual advantages. In case of limited data, it is not likely to estimate accurate and reliable predictions. Therefore. e-commerce sites holding insufficient ratings prefer offering predictions to their customers based on integrated data. However, users' preferences about products are considered online vendors' confidential and valuable assets; and they do not want to disclose them their partners during collaborative prediction processes.