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.

翻译：随着个性化推荐系统在信息过载时代变得至关重要，传统仅依赖历史用户交互的方法往往难以完全捕捉人类兴趣的多面性。为构建更以人为中心的用户偏好建模，本文提出了一种新型可解释推荐框架LLMHG，该框架协同融合了大语言模型的推理能力与超图神经网络的结构优势。通过有效剖析和解读个体用户兴趣的细微差异，本框架率先通过增强可解释性提升了推荐系统性能。我们验证了，明确考虑人类偏好的复杂性使得以人为中心且具备可解释性的LLMHG方法能持续超越各类真实数据集上的传统模型。所提出的即插即用增强框架不仅能立即带来推荐性能的提升，还为应用先进大语言模型更好捕捉机器学习应用中人类兴趣的复杂性提供了可行路径。