Graph-Based Approaches For Cold-Start Recommendation: A Comprehensive Analysis

The cold-start problem—where new users or items lack interaction history—remains one of the most critical challenges in recommender systems. This paper examines the evolution of recommendation techniques from rule-based and collaborative filtering to deep learning and the latest geometric deep learning (GDL) approaches. We analyze how graph-based models such as Graph Convolutional Networks (GCN), Graph Attention Networks (GAT), and LightGCN, along with knowledge-aware and hybrid strategies, address data sparsity and improve personalization. Special attention is given to inductive GNNs, meta-learning, and contrastive learning for rapid adaptation, as well as knowledge graph integration and multimodal embeddings for enhanced explainability. We provide a comparative evaluation of these methods under user and item cold-start scenarios, highlighting their strengths, lim- itations, and scalability in real-world environments. Our findings underscore the potential of GDL-based architectures and hybrid techniques to deliver robust, privacy-preserving, and adaptive recommendations in dynamic and heterogeneous contexts