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

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arxiv 2401.08217 v2 pith:W4HTFDOY submitted 2024-01-16 cs.IR

Zhixuan Chu , Yan Wang , Qing Cui , Longfei Li , Wenqing Chen , Zhan Qin , Kui Ren This is my paper
classification cs.IR
keywords recommendationexplainableframeworkhumanhuman-centricinterestsuseracross
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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.

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Cited by 5 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. HyperAgent4POI: Dynamic Semantic Message Passing on Multi-Agent Hypergraphs for Missing-Modality Recommendation

    cs.IR 2026-08 conditional novelty 6.0 of 10

    HyperAgent4POI couples per-layer LLM-based modality completion with soft hypergraph incidence refinement, reporting NDCG@20 gains of 7.4 to 9.4 percent over the strongest baseline at a 60 percent modality-missing rate.

  2. Hypergraph as Language

    cs.CL 2026-05 unverdicted novelty 6.0 of 10

    Hyper-Align is a hypergraph-native framework that serializes high-order relations into LLM-compatible tokens via HIDT-O templates and a HIP projector, outperforming graph-centric methods on HyperAlign-Bench.

  3. HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings

    stat.ML 2026-05 conditional novelty 6.0 of 10

    HYVINT generates hypergraphs by learning latent Poisson interaction intensities and diffusing hyperedge-side variational embeddings, with asymptotic generation-error bounds and improved structural fidelity in its repo...

  4. HYVINT: Intensity-Driven Hypergraph Generation with Variational Embeddings

    stat.ML 2026-05 unverdicted novelty 5.0 of 10

    HYVINT introduces an intensity-driven incidence mechanism and tractable variational estimator for hypergraph generation, with error bounds and empirical gains in fidelity, novelty, and diversity.

  5. DualHNIE: Dual-Channel Hypergraph Learning for Node Importance Estimation in Heterogeneous Knowledge Graphs

    cs.AI 2025-12 conditional novelty 4.0 of 10

    A dual-channel model (body title MetaHGNIE) scores node importance in heterogeneous knowledge graphs slightly above prior hypergraph baselines on four benchmarks, but several gains are within reported standard deviati...

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