Traditional recommender systems face several challenges, including the cold start problem and popularity bias, which can lead to less accurate recommendations. These limitations stem from their "black box" nature, making it difficult for users to understand why specific items are being recommended to them, leading to reduced trust in the system. In contrast, Large Language Model Reasoning Graphs (LLMRGs) offer a promising solution by providing more interpretable and context-aware recommendations through dynamic structures that use large language models to construct personalized graphs representing user interests through causal and logical inferences. LLMRGs capture higher-level semantic relationships between user profiles, behavioral data, and item features in an interpretable format, making them particularly effective in bridging the gap between making recommendations and explaining them. By providing transparency and ability to explain recommendations, LLMRGs can significantly impact user trust and satisfaction, ultimately driving better business outcomes through strategic metrics such as customer lifetime value measurement and churn reduction tracking.