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http://dx.doi.org/10.3837/tiis.2014.08.022

Privacy-Preserving Two-Party Collaborative Filtering on Overlapped Ratings  

Memis, Burak (Dept. of Computer Engineering, Dumlupinar University)
Yakut, Ibrahim (Dept. of Computer Engineering, Anadolu University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.8, no.8, 2014 , pp. 2948-2966 More about this Journal
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.
Keywords
Collaborative filtering; data scarcity; overlapped ratings; Pearson similarity; Slope one predictor; Privacy;
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