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Original research PRIVACY-PRESERVING COLLABORATIVE FILTERING FOR PERSONALIZED RECOMMENDATIONSPages 151-156
Abstract
Personalized recommendations have become very important for many online platforms. They aim to make the user experience better by offering content that matches each user's individual interests. This paper discusses the importance of personalized recommendations in different areas and their impact on keeping users engaged and satisfied. Through looking at existing research and case studies, we identify key challenges and opportunities in developing effective personalized recommendation systems. We also propose strategies to overcome these challenges and make personalized recommendations more effective. The other parts introduce the proposed system design, the technologies we will use, and how we will evaluate the methods. The paper concludes by presenting our current research progress.
Personalized recommendations have become a cornerstone of many online platforms, aiming to enhance user experience by offering content tailored to individual preferences. This paper explores the significance of personalized recommendations in various domains and discusses their impact on user engagement and satisfaction. Through an analysis of existing research and case studies, we identify key challenges and opportunities in developing effective personalized recommendation systems. Furthermore, we propose strategies to overcome these challenges and enhance the effectiveness of personalized recommendations. The other parts introduce the proposed system architecture, the technologies that we will use, and the evaluation methods. The paper is concluded by presenting the current research progress.
Keywords: Homomorphic encryption,
Federated learning, Differential privacy, Secure multiparty computation (SMC), Privacy-aware user preference modeling , Anonymized data sharing, Decentralized privacy-preserving recommendation models, Privacy-enhanced collaborative filtering, Encrypted user data, Secure aggregation.
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Elias Md Radzi, 
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