Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings

Creative Commons License

Memis B., Yakut I.

KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS, cilt.8, sa.8, ss.2948-2966, 2014 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 8 Sayı: 8
  • Basım Tarihi: 2014
  • Doi Numarası: 10.3837/tiis.2014.08.022
  • Dergi Adı: KSII TRANSACTIONS ON INTERNET AND INFORMATION SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2948-2966
  • Anahtar Kelimeler: Collaborative filtering, data scarcity, overlapped ratings, Pearson similarity, Slope one predictor, Privacy, RECOMMENDATIONS
  • Anadolu Üniversitesi Adresli: Evet

Özet

To promote recommendation services through prediction quality, some privacy-preserving collaborative filtering solutions are proposed to make e-commerce parties collaborate on partitioned data. It is almost probable that two parties hold ratings for the same users and items simultaneously; however, existing two-party privacy-preserving collaborative filtering solutions do not cover such overlaps. Since rating values and rated items are confidential, overlapping ratings make privacy-preservation more challenging. This study examines how to estimate predictions privately based on partitioned data with overlapped entries between two e-commerce companies. We consider both user-based and item-based collaborative filtering approaches and propose novel privacy-preserving collaborative filtering schemes in this sense. We also evaluate our schemes using real movie dataset, and the empirical outcomes show that the parties can promote collaborative services using our schemes.