The Deep Learning for Recommender System: Architecture, Advancements and Future Trends

Deep learning is becoming a game-changing technology in the field of recommender systems, which have grown as a result of the exponential growth of digital information and user data. This paper gives an extensive overview of deep learning architectures used in recommender systems, including emerging paradigms like Graph Neural Networks (GNNs), Autoencoders, Transformer-based models, and foundational models like Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNNs), Deep Neural Networks (DNNs). This study aims to examine how recommendation systems, including content-based filtering, collaborative filtering, and hybrid techniques, can be integrated with improvements in contextawareness, scalability, and customization. Moreover, it investigates advancements in multi-modal information integration, handling sparse data, and model interpretability. This paper outlines on future research directions that focus on fairness, transparency, and real-time adaptation while addressing current issues including overfitting, computational complexity, and cold-start challenges. It aims to help researchers and practitioners use deep learning for next-generation recommender systems using the latest developments in technology and trends.