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，传统仅依赖用户历史交互的方法往往难以全面捕捉人类兴趣的多面性。为了实现更以人为中心的用户偏好建模，本文提出了一种新颖的可解释推荐框架——LLMHG，该框架协同利用大型语言模型(LLM)的推理能力与超图神经网络的架构优势。通过有效剖析和解读个体用户兴趣的细微差别，我们的框架开创性地提升了推荐系统的可解释性增强方案。我们验证发现，显式考虑人类偏好的复杂性使得我们以人为中心且可解释的LLMHG方法在不同真实世界数据集上持续优于传统模型。所提出的即插即用增强框架在提升推荐性能的同时，为应用先进LLM以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。