Privacy-preserving SOM-based recommendations on horizontally distributed data

KALELİ C., Polat H.

KNOWLEDGE-BASED SYSTEMS, cilt.33, ss.124-135, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 33
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.knosys.2012.02.013
  • Dergi Adı: KNOWLEDGE-BASED SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.124-135
  • Anahtar Kelimeler: Privacy, Distributed data, Clustering, Recommendation, Performance
  • Anadolu Üniversitesi Adresli: Evet

Özet

To produce predictions with decent accuracy, collaborative filtering algorithms need sufficient data. Due to the nature of online shopping and increasing amount of online vendors, different customers' preferences about the same products can be distributed among various companies, even competing vendors. Therefore, those companies holding inadequate number of users' data might decide to combine their data in such a way to present accurate predictions with acceptable online performance. However, they do not want to divulge their data, because such data are considered confidential and valuable. Furthermore, it is not legal disclosing users' preferences: nevertheless, if privacy is protected, they can collaborate to produce correct predictions.