The movie recommendation systems have significantly enhanced users’ ability to discover new content. However, the opacity of these systems often leaves users questioning the rationale behind the suggestions. This study explores applying large language models (LLMs) to generate comprehensible and relevant explanations for movie recommendations. By integrating an LLM, specifically GPT-4, into a standard recommendation system, we aim to bridge the gap between complex recommendation algorithms and user understanding. The study involves a user-centric evaluation where participants interact with the system, receive movie recommendations, and are provided with LLM-generated explanations. User feedback is collected to assess the clarity, relevance, and overall satisfaction with these explanations. The results indicate that LLM-generated explanations significantly enhance user satisfaction and trust in the recommendation system compared to traditional methods. These findings suggest that incorporating LLMs can improve movie recommendation systems’ transparency and user experience, offering a promising direction for future enhancements.