A Review of Collaborative Filtering Recommendation Systems

Collaborative filtering (CF) has emerged as a cornerstone of modern recommendation systems, powering personalized user experiences in e-commerce, streaming services, social media, and news platforms. This paper provides a comprehensive review of CF-based recommendation models, covering traditional memory-based and model-based CF techniques, along with recent advances in deep learning-enhanced CF models. We discuss the challenges associated with CF, including data sparsity, cold start problems, scalability, and explainability. Furthermore, we analyze the impact of deep learning architectures such as neural collaborative filtering (NCF), autoencoders, graph neural networks (GNNS), and transformer-based models on CF performance. A comparative analysis of traditional and deep learning-based approaches is presented, alongside experimental insights from real-world deployments. Finally, we explore emerging trends such as multi-modal recommendation, reinforcement learning-driven CF, and real-time recommendation frameworks. This survey aims to guide future research and practical implementations in recommendation systems by highlighting key advancements, challenges, and promising directions.