• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings

Vol. 8, No. 8, August 28, 2014
10.3837/tiis.2014.08.022, Download Paper (Free):

Abstract

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.


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Cite this article

[IEEE Style]
B. Memis and I. Yakut, "Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings," KSII Transactions on Internet and Information Systems, vol. 8, no. 8, pp. 2948-2966, 2014. DOI: 10.3837/tiis.2014.08.022.

[ACM Style]
Burak Memis and Ibrahim Yakut. 2014. Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings. KSII Transactions on Internet and Information Systems, 8, 8, (2014), 2948-2966. DOI: 10.3837/tiis.2014.08.022.