Privacy-Preserving Collaborative Filtering on Overlapped Ratings


Memis B., Yakut I.

22nd IEEE International WETICE Conference (WETICE), Hammamet, Tunus, 17 - 20 Haziran 2013, ss.166-171 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/wetice.2013.55
  • Basıldığı Şehir: Hammamet
  • Basıldığı Ülke: Tunus
  • Sayfa Sayıları: ss.166-171
  • Anahtar Kelimeler: Collaborative Filtering, Data Scarcity, Overlapped Ratings, Privacy, RECOMMENDATIONS
  • Anadolu Üniversitesi Adresli: Evet

Özet

To promote recommendation services through prediction quality, there are some privacy-preserving collaborative filtering (PPCF) solutions enabling e-commerce parties to collaborate on partitioned data. It is almost probable that both parties hold ratings for the identical users and items simultaneously; however existing PPCF schemes have not explored such overlaps. Since rating values and rated items are confidential, overlapping ratings makes privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies and we propose novel PPCF schemes in this sense.