This paper introduces LLMHG, a novel explainable recommendation framework that integrates the reasoning capabilities of large language models (LLMs) with the structural advantages of hypergraph neural networks. The framework aims to enhance the modeling of human-centric preferences by profiling and interpreting the nuances of individual user interests. By leveraging LLMs, LLMHG can extract and categorize user preferences into structured representations called Interest Angles (IAs), which are then used to construct a multi-view hypergraph. This hypergraph captures the multifaceted nature of user interests and is refined through hypergraph structure learning techniques. The refined hypergraph is integrated with latent embeddings from conventional sequential recommendation models to improve recommendation performance. The proposed framework is validated on various real-world datasets, demonstrating superior performance compared to conventional models. Key contributions include a plug-and-play recommendation enhancement framework, the use of multi-view hypergraphs powered by LLMs, and a strategy for hypergraph structure optimization to refine LLM-based user profiling. The methodology is evaluated through comprehensive experiments, showing significant improvements in recommendation accuracy and explainability.This paper introduces LLMHG, a novel explainable recommendation framework that integrates the reasoning capabilities of large language models (LLMs) with the structural advantages of hypergraph neural networks. The framework aims to enhance the modeling of human-centric preferences by profiling and interpreting the nuances of individual user interests. By leveraging LLMs, LLMHG can extract and categorize user preferences into structured representations called Interest Angles (IAs), which are then used to construct a multi-view hypergraph. This hypergraph captures the multifaceted nature of user interests and is refined through hypergraph structure learning techniques. The refined hypergraph is integrated with latent embeddings from conventional sequential recommendation models to improve recommendation performance. The proposed framework is validated on various real-world datasets, demonstrating superior performance compared to conventional models. Key contributions include a plug-and-play recommendation enhancement framework, the use of multi-view hypergraphs powered by LLMs, and a strategy for hypergraph structure optimization to refine LLM-based user profiling. The methodology is evaluated through comprehensive experiments, showing significant improvements in recommendation accuracy and explainability.