A new hybrid recommendation algorithm with privacy

Abstract

Providing accurate and dependable recommendations efficiently while preserving privacy is essential for e-commerce sites to recruit new customers and keep the existing ones. Such sites might be able to increase their sales and profits while customers can obtain precise and trustworthy predictions if they use appropriate collaborative filtering (CF) algorithms without deeply jeopardizing users' privacy. We propose a new recommendation algorithm, which is a hybrid-memory and model-based algorithm to generate truthful referrals efficiently. Moreover, we use randomization techniques to preserve users' privacy while still offering CF services with decent accuracy. We perform real data-based trials and analyse our proposed schemes in terms of privacy, accuracy, and performance.