Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings
Burak Memis, İbrahim Yakut · KSII Transactions on Internet and Information Systems · 2014
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.