Recent advancements in large language models (LLMs) are transforming recommendation systems by integrating multiple data sources, user interactions, and innovative training methods. Traditional recommendation systems have been limited by their reliance on single data sources, which restricts their ability to understand complex user behaviors and item features. New methodologies, such as the zero-shot training-free recommendation system, enable LLMs to combine their capabilities with user interaction patterns, enhancing the quality of recommendations without the need for extensive fine-tuning. Furthermore, LLMs are being trained to recognize their limitations, reducing overconfidence in incorrect answers through techniques like PPO-M and PPO-C. Additionally, multi-agent dialogue trees are being utilized to help LLMs discern effective persuasion strategies, thereby improving their reasoning and decision-making processes. These developments signal a potential shift in how companies, including Walmart, leverage multimodal approaches for product recommendations, paving the way for innovative applications in the corporate sector.