Journal of Artificial Intelligence, Machine Learning and Soft Computing

Abstract

Recommendation and personalization systems have become integral components of modern digital platforms, enhancing user experiences across e-commerce, entertainment, social media, and online education. Machine Learning (ML) techniques lie at the core of these systems, enabling the prediction of user preferences and delivering tailored content. This paper provides a comprehensive review of ML approaches used in recommendation and personalization systems, including collaborative filtering, content-based filtering, hybrid methods, and deep learning approaches. Challenges such as scalability, cold-start problems, and bias in recommendations are also discussed. Future research directions, including reinforcement learning and federated recommendation systems, are highlighted. This review aims to provide researchers and practitioners with a consolidated understanding of current trends, methodologies, and applications in ML-driven recommendation systems.

Keywords: Machine Learning, Recommendation Systems, Personalization, Collaborative Filtering, Deep Learning, Hybrid Models, User Experience