Privacy-preserving top-N recommendation on horizontally partitioned data

Collaborative filtering techniques are widely used by many E-commerce sites for recommendation purposes. Such techniques help customers by suggesting products to purchase using other users' preferences. Today's top-N recommendation schemes are based on market basket data, which shows whether a customer bought an item or not. Data collected for recommendation purposes might be split between different parties. To provide better referrals and increase mutual advantages, such parties might want to share data. Due to privacy concerns, however, they do not want to disclose data. This paper presents a scheme for binary ratings-based top-N recommendation on horizontally partitioned data, in which two parties own disjoint sets of users' ratings for the same items while preserving data owners' privacy. If data owners want to produce referrals using the combined data while preserving their privacy, we propose a scheme to provide accurate top-N recommendations without exposing data owners' privacy. We conducted various experiments to evaluate our scheme and analyzed how different factors affect the performance using the experiment results.