This survey provides a comprehensive analysis of recent advancements in embedding techniques for recommender systems, covering centralized approaches like matrix, sequential, and graph-based models, along with solutions for scalability and emerging LLM-enhanced methods. It highlights their role in capturing complex relationships and improving recommendation performance.