mlsanityData to 7 Oct 2026

DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN

arXiv 2108.11883 ↗First posted 26 Aug 2021cs.LGcited by 19

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Abstract (arXiv)

In the information explosion era, recommender systems (RSs) are widely studied and applied to discover user-preferred information. A RS performs poorly when suffering from the cold-start issue, which can be alleviated if incorporating Knowledge Graphs (KGs) as side information. However, most existing works neglect the facts that node degrees in KGs are skewed and massive amount of interactions in KGs are recommendation-irrelevant. To address these problems, in this paper, we propose Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNN (DSKReG) that learns the relevance distribution of connected items from KGs and samples suitable items for recommendation following this distribution. We devise a differentiable sampling strategy, which enables the selection of relevant items to be jointly optimized with the model training procedure. The experimental results demonstrate that our model outperforms state-of-the-art KG-based recommender systems. The code is available online at https://github.com/YuWang-1024/DSKReG.

Key facts. Cited by 19 papers in the map (data through 7 Oct 2026). Builds on 30 of its 34 references that are in the map. First posted on arXiv 26 Aug 2021.

Builds on

30 of 34 references.

References · 30

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  2. 2017Graph Attention Networkscited by 8,854
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2016Categorical Reparameterization with Gumbel-Softmaxcited by 2,708
  5. 2017Neural Collaborative Filteringcited by 1,108
  6. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
  7. 2018Representation Learning on Graphs with Jumping Knowledge Networkscited by 875
  8. 2019Heterogeneous Graph Attention Networkcited by 732
  9. 2019DropEdge: Towards Deep Graph Convolutional Networks on Node Classificationcited by 679
  10. 2019Neural Graph Collaborative Filteringcited by 660
  11. 2019GraphSAINT: Graph Sampling Based Inductive Learning Methodcited by 618
  12. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
18 more
  1. 2019Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networkscited by 177
  2. 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 136
  3. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  4. 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
  5. 2020KG-BART: Knowledge Graph-Augmented BART for Generative Commonsense Reasoningcited by 106
  6. 2018Billion-scale Commodity Embedding for E-commerce Recommendation in Alibabacited by 105
  7. 2019Reparameterizable Subset Sampling via Continuous Relaxationscited by 69
  8. 2021Continuous-Time Sequential Recommendation with Temporal Graph Collaborative Transformercited by 69
  9. 2021Augmenting Sequential Recommendation with Pseudo-Prior Items via Reversely Pre-training Transformercited by 45
  10. 2020Reinforced Negative Sampling over Knowledge Graph for Recommendationcited by 40
  11. 2018Learning Heterogeneous Knowledge Base Embeddings for Explainable Recommendationcited by 39
  12. 2021ConsisRec: Enhancing GNN for Social Recommendation via Consistent Neighbor Aggregationcited by 39
  13. 2021Modeling Sequences as Distributions with Uncertainty for Sequential Recommendationcited by 22
  14. 2020BasConv: Aggregating Heterogeneous Interactions for Basket Recommendation with Graph Convolutional Neural Networkcited by 16
  15. 2019JSCN: Joint Spectral Convolutional Network for Cross Domain Recommendationcited by 8
  16. 2020ATBRG: Adaptive Target-Behavior Relational Graph Network for Effective Recommendationcited by 8
  17. 2021Explicit Pairwise Factorized Graph Neural Network for Semi-Supervised Node Classificationcited by 3
  18. 2018Unsupervised Meta-path Reduction on Heterogeneous Information Networkscited by 2

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 4; 2023: 7; 2024: 5; 2025: 1; 2026: 2.

19 papers.

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  1. 2022Sequential Recommendation via Stochastic Self-Attentioncited by 37
  2. 2023Counterfactual Learning on Graphs: A Surveycited by 22
  3. 2023Data-centric Graph Learning: A Surveycited by 22
  4. 2022ContrastVAE: Contrastive Variational AutoEncoder for Sequential Recommendationcited by 19
  5. 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
  6. 2024Does Negative Sampling Matter? A Review with Insights into its Theory and Applicationscited by 14
  7. 2023GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networkscited by 7
  8. 2022Sequential Recommendation with Auxiliary Item Relationships via Multi-Relational Transformercited by 3
  9. 2023Episodes Discovery Recommendation with Multi-Source Augmentationscited by 3
  10. 2025KERAG_R: Knowledge-Enhanced Retrieval-Augmented Generation for Recommendationcited by 3
  11. 2023A Comprehensive Survey on Deep Learning Techniques in Educational Data Miningcited by 2
  12. 2022Bayesian Negative Sampling for Recommendationcited by 1
7 more
  1. 2023Knowledge Graph Context-Enhanced Diversified Recommendationcited by 1
  2. 2024KG4RecEval: Does Knowledge Graph Really Matter for Recommender Systems?cited by 1
  3. 2024Knowledge Graph Pruning for Recommendationcited by 1
  4. 2024Top K Enhanced Reinforcement Learning Attacks on Heterogeneous Graph Node Classificationcited by 0
  5. 2024TIGER: Temporally Improved Graph Entity Linkercited by 0
  6. 2026Blackknife: Hard-Label Query-Limited Black-Box Attacks on Heterogeneous Graph Neural Networkscited by 0
  7. 2026Knowledge Graphs Meet Graph Neural Networks: A Comprehensive Surveycited by 0