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

TL;DR

This paper introduces LLMHG, a novel explainable recommendation framework that combines large language models and hypergraph neural networks to better capture and interpret human preferences, improving recommendation accuracy and explainability.

Contribution

It presents a new human-centric, explainable recommendation framework that integrates LLMs with hypergraph neural networks, enhancing interpretability and performance.

Findings

Outperforms conventional models on real-world datasets

Enhances recommendation explainability through human interest profiling

Provides a plug-and-play framework for improved recommendation systems

Abstract

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.