  As personalized recommendation systems become vital in the age of information
overload, traditional methods relying solely on historical user interactions
often fail to fully capture the multifaceted nature of human interests. To
enable more human-centric modeling of user preferences, this work proposes a
novel explainable recommendation framework, i.e., LLMHG, synergizing the
reasoning capabilities of large language models (LLMs) and the structural
advantages of hypergraph neural networks. By effectively profiling and
interpreting the nuances of individual user interests, our framework pioneers
enhancements to recommendation systems with increased explainability. We
validate that explicitly accounting for the intricacies of human preferences
allows our human-centric and explainable LLMHG approach to consistently
outperform conventional models across diverse real-world datasets. The proposed
plug-and-play enhancement framework delivers immediate gains in recommendation
performance while offering a pathway to apply advanced LLMs for better
capturing the complexity of human interests across machine learning
applications.
