AI Based Privacy Preserving Cross Domain Recommendation System

Abstract

Recommendation systems have become an essential component in modern digital platforms, helping users efficiently navigate large volumes of information. This survey paper focuses on Artificial Intelligence (AI)-based recommendation systems, particularly in domains such as e-learning and cross-domain applications, where they provide personalized suggestions based on user preferences and behavior using techniques like collaborative filtering, content-based filtering, and hybrid approaches. Recent advancements such as deep learning, federated learning, and cross-domain recommendation techniques have further improved the accuracy and efficiency of these systems while also addressing privacy concerns. However, several challenges still exist, including data sparsity, lack of contextual understanding, and scalability issues. In addition to technical challenges, ethical considerations such as privacy, data security, bias, and transparency have become increasingly important in the design and deployment of recommendation systems. This survey also identifies current research gaps and highlights future directions, including the integration of real-time data and advanced AI models. Overall, the paper provides a comprehensive overview of AI-based recommendation systems, their applications, limitations, and potential future developments.

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