# Self-Supervised Hypergraph Transformer for Recommender Systems

arXiv: https://arxiv.org/abs/2207.14338 · HTML: https://mlsanity.com/paper/2207.14338/

## Key facts

- Cited by 32 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 42 references that are in the map.
- First posted on arXiv 28 Jul 2022.

## Builds on (most cited first, 30 of 30)

- [Simplifying Graph Convolutional Networks](https://mlsanity.com/paper/1902.07153/) (2019)
- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Graph Convolutional Neural Networks for Web-Scale Recommender Systems](https://mlsanity.com/paper/1806.01973/) (2018)
- [Neural Collaborative Filtering](https://mlsanity.com/paper/1708.05031/) (2017)
- [Deep Graph Infomax](https://mlsanity.com/paper/1809.10341/) (2018)
- [BERT4Rec: Sequential Recommendation with Bidirectional Encoder Representations from Transformer](https://mlsanity.com/paper/1904.06690/) (2019)
- [Heterogeneous Graph Attention Network](https://mlsanity.com/paper/1903.07293/) (2019)
- [Self-supervised Learning: Generative or Contrastive](https://mlsanity.com/paper/2006.08218/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Variational Autoencoders for Collaborative Filtering](https://mlsanity.com/paper/1802.05814/) (2018)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Graph Representation Learning via Graphical Mutual Information Maximization](https://mlsanity.com/paper/2002.01169/) (2020)
- [Neural Collaborative Filtering vs. Matrix Factorization Revisited](https://mlsanity.com/paper/2005.09683/) (2020)
- [Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approach](https://mlsanity.com/paper/2001.10167/) (2020)
- [Disentangled Graph Collaborative Filtering](https://mlsanity.com/paper/2007.01764/) (2020)
- [Causal Intervention for Leveraging Popularity Bias in Recommendation](https://mlsanity.com/paper/2105.06067/) (2021)
- [Self-Supervised Multi-Channel Hypergraph Convolutional Network for Social Recommendation](https://mlsanity.com/paper/2101.06448/) (2021)
- [Knowledge Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2205.00976/) (2022)
- [Hypergraph Contrastive Collaborative Filtering](https://mlsanity.com/paper/2204.12200/) (2022)
- [STAR-GCN: Stacked and Reconstructed Graph Convolutional Networks for Recommender Systems](https://mlsanity.com/paper/1905.13129/) (2019)
- [Knowledge-aware Coupled Graph Neural Network for Social Recommendation](https://mlsanity.com/paper/2110.03987/) (2021)
- [Knowledge-Enhanced Hierarchical Graph Transformer Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.04000/) (2021)
- [Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs](https://mlsanity.com/paper/2007.08294/) (2020)
- [A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation](https://mlsanity.com/paper/2010.15982/) (2020)
- [Graph Meta Network for Multi-Behavior Recommendation](https://mlsanity.com/paper/2110.03969/) (2021)
- [DisenHAN: Disentangled Heterogeneous Graph Attention Network for Recommendation](https://mlsanity.com/paper/2106.10879/) (2021)
- [Sequential Recommendation with Self-Attentive Multi-Adversarial Network](https://mlsanity.com/paper/2005.10602/) (2020)
- [Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation](https://mlsanity.com/paper/2109.02859/) (2021)
- [Concept-Aware Denoising Graph Neural Network for Micro-Video Recommendation](https://mlsanity.com/paper/2109.13527/) (2021)
- [Recent Advances in Heterogeneous Relation Learning for Recommendation](https://mlsanity.com/paper/2110.03455/) (2021)

## Built on by (most cited first, 30 of 32)

- [LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2302.08191/) (2023)
- [Graph Transformers: A Survey](https://mlsanity.com/paper/2407.09777/) (2024)
- [Graph Masked Autoencoder for Sequential Recommendation](https://mlsanity.com/paper/2305.04619/) (2023)
- [HypeBoy: Generative Self-Supervised Representation Learning on Hypergraphs](https://mlsanity.com/paper/2404.00638/) (2024)
- [A Survey on Fairness-aware Recommender Systems](https://mlsanity.com/paper/2306.00403/) (2023)
- [TransGNN: Harnessing the Collaborative Power of Transformers and Graph Neural Networks for Recommender Systems](https://mlsanity.com/paper/2308.14355/) (2023)
- [RevGNN: Negative Sampling Enhanced Contrastive Graph Learning for Academic Reviewer Recommendation](https://mlsanity.com/paper/2407.20684/) (2024)
- [Hypergraph Self-supervised Learning with Sampling-efficient Signals](https://mlsanity.com/paper/2404.11825/) (2024)
- [A Comprehensive Survey on Self-Supervised Learning for Recommendation](https://mlsanity.com/paper/2404.03354/) (2024)
- [Space-aware Socioeconomic Indicator Inference with Heterogeneous Graphs](https://mlsanity.com/paper/2405.14135/) (2024)
- [Graph Learning and Its Advancements on Large Language Models: A Holistic Survey](https://mlsanity.com/paper/2212.08966/) (2022)
- [How Expressive are Graph Neural Networks in Recommendation?](https://mlsanity.com/paper/2308.11127/) (2023)
- [RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2312.16563/) (2023)
- [Tackling Over-smoothing on Hypergraphs: A Ricci Flow-guided Neural Diffusion Approach](https://mlsanity.com/paper/2603.15696/) (2026)
- [Topology-guided Hypergraph Transformer Network: Unveiling Structural Insights for Improved Representation](https://mlsanity.com/paper/2310.09657/) (2023)
- [COOL: A Conjoint Perspective on Spatio-Temporal Graph Neural Network for Traffic Forecasting](https://mlsanity.com/paper/2403.01091/) (2024)
- [HiTeC: Hierarchical Contrastive Learning on Text-Attributed Hypergraph with Semantic-Aware Augmentation](https://mlsanity.com/paper/2508.03104/) (2025)
- [Capturing Popularity Trends: A Simplistic Non-Personalized Approach for Enhanced Item Recommendation](https://mlsanity.com/paper/2308.08799/) (2023)
- [Heterogeneous Hypergraph Embedding for Recommendation Systems](https://mlsanity.com/paper/2407.03665/) (2024)
- [SiMilarity-Enhanced Homophily for Multi-View Heterophilous Graph Clustering](https://mlsanity.com/paper/2410.03596/) (2024)
- [Handling Heterophily in Recommender Systems with Wavelet Hypergraph Diffusion](https://mlsanity.com/paper/2501.14399/) (2025)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [Hypergraph Enhanced Knowledge Tree Prompt Learning for Next-Basket Recommendation](https://mlsanity.com/paper/2312.15851/) (2023)
- [Matrix Completion with Hypergraphs:Sharp Thresholds and Efficient Algorithms](https://mlsanity.com/paper/2401.08197/) (2024)
- [Bilateral Unsymmetrical Graph Contrastive Learning for Recommendation](https://mlsanity.com/paper/2403.15075/) (2024)
- [HyperSMOTE: A Hypergraph-based Oversampling Approach for Imbalanced Node Classifications](https://mlsanity.com/paper/2409.05402/) (2024)
- [Hypergraph Diffusion for High-Order Recommender Systems](https://mlsanity.com/paper/2501.16722/) (2025)
- [Academic Network Representation via Prediction-Sampling Incorporated Tensor Factorization](https://mlsanity.com/paper/2504.08323/) (2025)
- [Structure-aware Hypergraph Transformer for Diagnosis Prediction in Electronic Health Records](https://mlsanity.com/paper/2508.20500/) (2025)
- [Hypergraph Contrastive Learning for both Homophilic and Heterophilic Hypergraphs](https://mlsanity.com/paper/2511.18783/) (2025)

## Abstract (arXiv)

Graph Neural Networks (GNNs) have been shown as promising solutions for collaborative filtering (CF) with the modeling of user-item interaction graphs. The key idea of existing GNN-based recommender systems is to recursively perform the message passing along the user-item interaction edge for refining the encoded embeddings. Despite their effectiveness, however, most of the current recommendation models rely on sufficient and high-quality training data, such that the learned representations can well capture accurate user preference. User behavior data in many practical recommendation scenarios is often noisy and exhibits skewed distribution, which may result in suboptimal representation performance in GNN-based models. In this paper, we propose SHT, a novel Self-Supervised Hypergraph Transformer framework (SHT) which augments user representations by exploring the global collaborative relationships in an explicit way. Specifically, we first empower the graph neural CF paradigm to maintain global collaborative effects among users and items with a hypergraph transformer network. With the distilled global context, a cross-view generative self-supervised learning component is proposed for data augmentation over the user-item interaction graph, so as to enhance the robustness of recommender systems. Extensive experiments demonstrate that SHT can significantly improve the performance over various state-of-the-art baselines. Further ablation studies show the superior representation ability of our SHT recommendation framework in alleviating the data sparsity and noise issues. The source code and evaluation datasets are available at: https://github.com/akaxlh/SHT.