Providing naïve Bayesian classifier-based private recommendations on partitioned data

Abstract

Data collected for collaborative filtering (CF) purposes might be split between various parties. Integrating such data is helpful for both e-companies and customers due to mutual advantageous. However, due to privacy reasons, data owners do not want to disclose their data. We hypothesize that if privacy measures are provided, data holders might decide to integrate their data to perform richer CF services. In this paper, we investigate how to achieve naïve Bayesian classifier (NBC)-based CF tasks on partitioned data with privacy. We perform experiments on real data, analyze our outcomes, and provide some suggestions. © Springer-Verlag Berlin Heidelberg 2007.