Privacy Preserving Recommender Systems

Abstract

Privacy-preserving recommender systems are a growing area of research and development due to concerns about user privacy in digital environments. This review paper examines the existing methodologies and techniques used in designing and implementing these systems, focusing on their application in e-commerce, social media, and personalized content delivery platforms. The paper discusses the fundamental principles of privacy-preserving recommender systems and the motivations behind their need. The review also highlights the challenges and opportunities associated with existing privacy-preserving recommender systems, including scalability, efficiency, and usability. In this review, we focus on the challenges and opportunities that come with recommendation systems, and compare different systems to see how well they scale up, how fast they work, and how easy they are to use.