# Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph

arXiv: https://arxiv.org/abs/2308.07752 · HTML: https://mlsanity.com/paper/2308.07752/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2023.

## Built on by (most cited first, 10 of 10)

- [Ada-HGNN: Adaptive Sampling for Scalable Hypergraph Neural Networks](https://mlsanity.com/paper/2405.13372/) (2024)
- [A Survey of Link Prediction in N-ary Knowledge Graphs](https://mlsanity.com/paper/2506.08970/) (2025)
- [MMHCL: Multi-Modal Hypergraph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2504.16576/) (2025)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Understanding the Embedding Models on Hyper-relational Knowledge Graph](https://mlsanity.com/paper/2508.03280/) (2025)
- [Enhancing Recommendation with Denoising Auxiliary Task](https://mlsanity.com/paper/2409.17402/) (2024)
- [AGP: A Novel Arabidopsis thaliana Genomics-Phenomics Dataset and its HyperGraph Baseline Benchmarking](https://mlsanity.com/paper/2508.14934/) (2025)
- [Heterophily-Aware Adaptive Knowledge Distillation for Hypergraph Neural Networks](https://mlsanity.com/paper/2606.08978/) (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)
- [Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Survey](https://mlsanity.com/paper/2607.09666/) (2026)

## Abstract (arXiv)

Knowledge graphs (KGs) are commonly used as side information to enhance collaborative signals and improve recommendation quality. In the context of knowledge-aware recommendation (KGR), graph neural networks (GNNs) have emerged as promising solutions for modeling factual and semantic information in KGs. However, the long-tail distribution of entities leads to sparsity in supervision signals, which weakens the quality of item representation when utilizing KG enhancement. Additionally, the binary relation representation of KGs simplifies hyper-relational facts, making it challenging to model complex real-world information. Furthermore, the over-smoothing phenomenon results in indistinguishable representations and information loss. To address these challenges, we propose the SDK (Self-Supervised Dynamic Hypergraph Recommendation based on Hyper-Relational Knowledge Graph) framework. This framework establishes a cross-view hypergraph self-supervised learning mechanism for KG enhancement. Specifically, we model hyper-relational facts in KGs to capture interdependencies between entities under complete semantic conditions. With the refined representation, a hypergraph is dynamically constructed to preserve features in the deep vector space, thereby alleviating the over-smoothing problem. Furthermore, we mine external supervision signals from both the global perspective of the hypergraph and the local perspective of collaborative filtering (CF) to guide the model prediction process. Extensive experiments conducted on different datasets demonstrate the superiority of the SDK framework over state-of-the-art models. The results showcase its ability to alleviate the effects of over-smoothing and supervision signal sparsity.