Estimating NBC-based recommendations on arbitrarily partitioned data with privacy
KNOWLEDGE-BASED SYSTEMS, cilt.36, ss.353-362, 2012 (SCI-Expanded, Scopus)
- 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.