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

arXiv: https://arxiv.org/abs/2401.08217 · HTML: https://mlsanity.com/paper/2401.08217/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2024.

## Built on by (most cited first, 19 of 19)

- [A Survey on Medical Large Language Models: Technology, Application, Trustworthiness, and Future Directions](https://mlsanity.com/paper/2406.03712/) (2024)
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- [Neutralizing Popularity Bias in LLM-based Recommendation via Counterfactual Reasoning Guidelines](https://mlsanity.com/paper/2503.08051/) (2025)
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- [AGP: A Novel Arabidopsis thaliana Genomics-Phenomics Dataset and its HyperGraph Baseline Benchmarking](https://mlsanity.com/paper/2508.14934/) (2025)
- [HyperGVL: Benchmarking and Improving Large Vision-Language Models in Hypergraph Understanding and Reasoning](https://mlsanity.com/paper/2604.15648/) (2026)
- [HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings](https://mlsanity.com/paper/2605.16836/) (2026)
- [Hypergraph as Language](https://mlsanity.com/paper/2605.21858/) (2026)
- [GRAFT: Biological Graph and Hypergraph Benchmarks for Linked Gene Expression and Phenotypic Trait Prediction in Arabidopsis thaliana](https://mlsanity.com/paper/2606.27413/) (2026)
- [HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation](https://mlsanity.com/paper/2608.01846/) (2026)

## Abstract (arXiv)

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.