Privacy-preserving concordance-based recommendations on vertically distributed data

Recommender systems are attractive components of e-commerce. Customers apply such systems to get help for choosing the appropriate product to purchase. To provide accurate and dependable referrals, recommender systems require sufficient user data. On the other hand, since people purchase products from different online vendors, collected user data for recommendation purposes might be distributed among several e-companies. Consequently, due to distributed data, such companies having inadequate data cannot provide truthful predictions. To overcome this challenge, data holders might want to collaborate. However, due to privacy and financial fears, they might hesitate to partnership. In this paper, we propose a concordance measure-based solution that enables data holders to produce recommendations without jeopardizing their privacy. We perform real data set-based experiments and analyze the solution in terms of privacy and extra costs. The experimental results show that e-companies can produce more accurate recommendations by employing the provided scheme.