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As personalized recommendation systems become vital in the age of informationoverload, traditional methods relying solely on historical user interactionsoften fail to fully capture the multifaceted nature of human interests. Toenable more human-centric modeling of user preferences, this work proposes anovel explainable recommendation framework, i.e., LLMHG, synergizing thereasoning capabilities of large language models (LLMs) and the structuraladvantages of hypergraph neural networks. By effectively profiling andinterpreting the nuances of individual user interests, our framework pioneersenhancements to recommendation systems with increased explainability. Wevalidate that explicitly accounting for the intricacies of human preferencesallows our human-centric and explainable LLMHG approach to consistentlyoutperform conventional models across diverse real-world datasets. The proposedplug-and-play enhancement framework delivers immediate gains in recommendationperformance while offering a pathway to apply advanced LLMs for bettercapturing the complexity of human interests across machine learningapplications.

LLM-Guided Multi-View Hypergraph Learning for Human-Centric Explainable Recommendation