mlsanityData to 7 Oct 2026

Graph Convolutional Network for Recommendation with Low-pass Collaborative Filters

arXiv 2006.15516 ↗First posted 28 Jun 2020cs.LG cs.IR stat.MLcited by 32

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Abstract (arXiv)

Graph Convolutional Network (GCN) is widely used in graph data learning tasks such as recommendation. However, when facing a large graph, the graph convolution is very computationally expensive thus is simplified in all existing GCNs, yet is seriously impaired due to the oversimplification. To address this gap, we leverage the textitoriginal graph convolution in GCN and propose a Low-pass Collaborative Filter (LCF) to make it applicable to the large graph. LCF is designed to remove the noise caused by exposure and quantization in the observed data, and it also reduces the complexity of graph convolution in an unscathed way. Experiments show that LCF improves the effectiveness and efficiency of graph convolution and our GCN outperforms existing GCNs significantly. Codes are available on urlhttps://github.com/Wenhui-Yu/LCFN.

Key facts. Cited by 32 papers in the map (data through 7 Oct 2026). First posted on arXiv 28 Jun 2020.

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Citing papers per year: 2021: 6; 2022: 5; 2023: 6; 2024: 9; 2025: 3; 2026: 3.

32 papers.

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  1. 2021SimpleX: A Simple and Strong Baseline for Collaborative Filteringcited by 65
  2. 2021Graph Learning based Recommender Systems: A Reviewcited by 59
  3. 2021How Powerful is Graph Convolution for Recommendation?cited by 50
  4. 2021Sparse-Interest Network for Sequential Recommendationcited by 37
  5. 2024How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspectivecited by 21
  6. 2023Adap-τ: Adaptively Modulating Embedding Magnitude for Recommendationcited by 19
  7. 2021Causal Incremental Graph Convolution for Recommender System Retrainingcited by 14
  8. 2022Parameter-free Dynamic Graph Embedding for Link Predictioncited by 12
  9. 2021Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classificationcited by 10
  10. 2024PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendationcited by 9
  11. 2023AGNN: Alternating Graph-Regularized Neural Networks to Alleviate Over-Smoothingcited by 7
  12. 2024Layer-diverse Negative Sampling for Graph Neural Networkscited by 7
20 more
  1. 2025You Are What You Bought: Generating Customer Personas for E-commerce Applicationscited by 7
  2. 2022DisenHCN: Disentangled Hypergraph Convolutional Networks for Spatiotemporal Activity Predictioncited by 5
  3. 2023BSL: Understanding and Improving Softmax Loss for Recommendationcited by 5
  4. 2023Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysiscited by 3
  5. 2024Frequency-aware Graph Signal Processing for Collaborative Filteringcited by 3
  6. 2024Graph Neural Networks Are More Than Filters: Revisiting and Benchmarking from A Spectral Perspectivecited by 3
  7. 2023Time-aware Hyperbolic Graph Attention Network for Session-based Recommendationcited by 2
  8. 2024Signal-SGN: A Spiking Graph Convolutional Network for Skeletal Action Recognition via Learning Temporal-Frequency Dynamicscited by 2
  9. 2025Training-free Adjustable Polynomial Graph Filtering for Ultra-fast Multimodal Recommendationcited by 2
  10. 2025Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solutioncited by 2
  11. 2022GPNet: Simplifying Graph Neural Networks via Multi-channel Geometric Polynomialscited by 1
  12. 2024Graph Contrastive Learning with Low-Rank Regularization and Low-Rank Attention for Noisy Node Classificationcited by 1
  13. 2026Talos: Optimizing Top-K Accuracy in Recommender Systemscited by 1
  14. 2022Stratified Graph Spectracited by 0
  15. 2022Density-Aware Hyper-Graph Neural Networks for Graph-based Semi-supervised Node Classificationcited by 0
  16. 2023DualHGNN: A Dual Hypergraph Neural Network for Semi-Supervised Node Classification based on Multi-View Learning and Density Awarenesscited by 0
  17. 2024Harmonizing Feature Maps: A Graph Convolutional Approach for Enhancing Adversarial Robustnesscited by 0
  18. 2024A Sinkhorn Regularized Adversarial Network for Image Guided DEM Super-resolution using Frequency Selective Hybrid Graph Transformercited by 0
  19. 2026Correct and Weight: A Simple Yet Effective Loss for Implicit Feedback Recommendationcited by 0
  20. 2026Low-pass Personalized Subgraph Federated Recommendationcited by 0