Privacy-Preserving Collaborative Filtering on Overlapped Ratings

Memis B., Yakut I.

22nd IEEE International WETICE Conference (WETICE), Hammamet, Tunisia, 17 - 20 June 2013, pp.166-171, (Full Text) identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/wetice.2013.55
  • City: Hammamet
  • Country: Tunisia
  • Page Numbers: pp.166-171
  • Keywords: Collaborative Filtering, Data Scarcity, Overlapped Ratings, Privacy, RECOMMENDATIONS
  • Anadolu University Affiliated: Yes

Abstract

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.