RecommenderSystems: Architectures, Applications, and FutureDirections a Systematic Survey

Recommender systems have become essential infrastructure in today's digital world,
enabling personalized experiences in domains ranging from e-commerce to healthcare. This paper
surveys the main algorithmic approaches collaborative filtering, content-based filtering, hybrid
methods, and deep learning architectures alongside their real-world applications and evaluation
methods. We also examine pressing challenges such as data sparsity, cold-start, fairness, and
explainability. Our review indicates that the next wave of advances will come from integrating large
language models (LLMs), graph neural networks (GNNs), and causal reasoning into recommendation
pipelines.