Privacy-Preserving Collaborative Filtering on Overlapped Ratings
Burak Memis, İbrahim Yakut · 2013
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.