mlsanityData to 7 Oct 2026

Graph Trend Filtering Networks for Recommendations

arXiv 2108.05552 ↗First posted 12 Aug 2021cs.IR cs.AI cs.LGcited by 53

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Abstract (arXiv)

Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online behaviors, e.g., clicks, add-to-cart, purchases, etc. To exploit these user-item interactions, there are increasing efforts on considering the user-item interactions as a user-item bipartite graph and then performing information propagation in the graph via Graph Neural Networks (GNNs). Given the power of GNNs in graph representation learning, these GNNs-based recommendation methods have remarkably boosted the recommendation performance. Despite their success, most existing GNNs-based recommender systems overlook the existence of interactions caused by unreliable behaviors (e.g., random/bait clicks) and uniformly treat all the interactions, which can lead to sub-optimal and unstable performance. In this paper, we investigate the drawbacks (e.g., non-adaptive propagation and non-robustness) of existing GNN-based recommendation methods. To address these drawbacks, we introduce a principled graph trend collaborative filtering method and propose the Graph Trend Filtering Networks for recommendations (GTN) that can capture the adaptive reliability of the interactions. Comprehensive experiments and ablation studies are presented to verify and understand the effectiveness of the proposed framework. Our implementation based on PyTorch is available at https://github.com/wenqifan03/GTN-SIGIR2022.

Key facts. Cited by 53 papers in the map (data through 7 Oct 2026). Builds on 32 of its 58 references that are in the map. First posted on arXiv 12 Aug 2021.

Builds on

32 of 58 references.

References · 32

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  3. 2017Inductive Representation Learning on Large Graphscited by 6,284
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  6. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  7. 2017Neural Collaborative Filteringcited by 1,108
  8. 2019Graph Neural Networks for Social Recommendationcited by 711
  9. 2019Neural Graph Collaborative Filteringcited by 660
  10. 2020Graph Structure Learning for Robust Graph Neural Networkscited by 368
  11. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  12. 2018Variational Autoencoders for Collaborative Filteringcited by 308
20 more
  1. 2020Self-supervised Graph Learning for Recommendationcited by 305
  2. 2020Disentangled Graph Collaborative Filteringcited by 118
  3. 2019Sequential Recommender Systems: Challenges, Progress and Prospectscited by 115
  4. 2020Node Similarity Preserving Graph Convolutional Networkscited by 112
  5. 2020A Unified View on Graph Neural Networks as Graph Signal Denoisingcited by 110
  6. 2014Collaborative Deep Learning for Recommender Systemscited by 95
  7. 2021Trustworthy AI: A Computational Perspectivecited by 93
  8. 2019Does Gender Matter? Towards Fairness in Dialogue Systemscited by 89
  9. 2018Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learningcited by 71
  10. 2021Elastic Graph Neural Networkscited by 67
  11. 2021Automated Self-Supervised Learning for Graphscited by 50
  12. 2020Clicks can be Cheating: Counterfactual Recommendation for Mitigating Clickbait Issuecited by 48
  13. 2013Adaptive piecewise polynomial estimation via trend filteringcited by 47
  14. 2020AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendationscited by 47
  15. 2014Trend Filtering on Graphscited by 45
  16. 2019Deep Social Collaborative Filteringcited by 32
  17. 2020Attacking Black-box Recommendations via Copying Cross-domain User Profilescited by 31
  18. 2021AutoLoss: Automated Loss Function Search in Recommendationscited by 31
  19. 2019Deep Adversarial Social Recommendationcited by 20
  20. 2021Jointly Attacking Graph Neural Network and its Explanationscited by 18

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 9; 2023: 13; 2024: 15; 2025: 13; 2026: 3.

53 papers.

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  1. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 450
  2. 2023Recommender Systems in the Era of Large Language Models (LLMs)cited by 188
  3. 2022Condensing Graphs via One-Step Gradient Matchingcited by 100
  4. 2023Generative Diffusion Models on Graphs: Methods and Applicationscited by 79
  5. 2025RouterEval: A Comprehensive Benchmark for Routing LLMs to Explore Model-level Scaling Up in LLMscited by 58
  6. 2023Amazon-M2: A Multilingual Multi-locale Shopping Session Dataset for Recommendation and Text Generationcited by 42
  7. 2022Linkless Link Prediction via Relational Distillationcited by 39
  8. 2024A Survey of Mambacited by 39
  9. 2023Fast Graph Condensation with Structure-based Neural Tangent Kernelcited by 36
  10. 2022Blurring-Sharpening Process Models for Collaborative Filteringcited by 35
  11. 2024TokenRec: Learning to Tokenize ID for LLM-based Generative Recommendationcited by 35
  12. 2022A Comprehensive Survey on Trustworthy Recommender Systemscited by 32
38 more
  1. 2024Linear-Time Graph Neural Networks for Scalable Recommendationscited by 31
  2. 2022Disentangled Contrastive Learning for Social Recommendationcited by 27
  3. 2022Knowledge-enhanced Black-box Attacks for Recommendationscited by 25
  4. 2023Graph Signal Diffusion Model for Collaborative Filteringcited by 25
  5. 2025CheatAgent: Attacking LLM-Empowered Recommender Systems via LLM Agentcited by 22
  6. 2024Graph Unlearning with Efficient Partial Retrainingcited by 20
  7. 2023Graph Collaborative Signals Denoising and Augmentation for Recommendationcited by 16
  8. 2022Generating Counterfactual Hard Negative Samples for Graph Contrastive Learningcited by 13
  9. 2024Investigating Out-of-Distribution Generalization of GNNs: An Architecture Perspectivecited by 13
  10. 2022Collaboration-Aware Graph Convolutional Network for Recommender Systemscited by 12
  11. 2024RoarGraph: A Projected Bipartite Graph for Efficient Cross-Modal Approximate Nearest Neighbor Searchcited by 12
  12. 2022Link Prediction with Non-Contrastive Learningcited by 11
  13. 2023On Manipulating Signals of User-Item Graph: A Jacobi Polynomial-based Graph Collaborative Filteringcited by 9
  14. 2023Message Intercommunication for Inductive Relation Reasoningcited by 8
  15. 2025Diffusion Generative Recommendation with Continuous Tokenscited by 8
  16. 2025Retrieval-Augmented Purifier for Robust LLM-Empowered Recommendationcited by 7
  17. 2025Graph Spectral Filtering with Chebyshev Interpolation for Recommendationcited by 7
  18. 2024MerRec: A Large-scale Multipurpose Mercari Dataset for Consumer-to-Consumer Recommendation Systemscited by 6
  19. 2025Understanding and Mitigating Under-Confidence in GNNs from the Final Layercited by 6
  20. 2023CARL-G: Clustering-Accelerated Representation Learning on Graphscited by 5
  21. 2023Graph Neural Ordinary Differential Equations-based method for Collaborative Filteringcited by 5
  22. 2024Node Duplication Improves Cold-start Link Predictioncited by 4
  23. 2024Graph Augmentation for Recommendationcited by 4
  24. 2024SCONE: A Novel Stochastic Sampling to Generate Contrastive Views and Hard Negative Samples for Recommendationcited by 4
  25. 2023Challenging the Myth of Graph Collaborative Filtering: a Reasoned and Reproducibility-driven Analysiscited by 3
  26. 2023RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendationcited by 3
  27. 2024A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systemscited by 3
  28. 2025Graph Defense Diffusion Modelcited by 3
  29. 2025Continuous-time Discrete-space Diffusion Model for Recommendationcited by 3
  30. 2026Enhancing Molecular Property Predictions by Learning from Bond Modelling and Interactionscited by 3
  31. 2023Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Frameworkcited by 2
  32. 2025Prompt-Based LLMs for Position Bias-Aware Reranking in Personalized Recommendationscited by 1
  33. 2024Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendationcited by 0
  34. 2024Sub-graph Based Diffusion Model for Link Predictioncited by 0
  35. 2024MixRec: Heterogeneous Graph Collaborative Filteringcited by 0
  36. 2025Leap: Inductive Link Prediction via Learnable TopologyAugmentationcited by 0
  37. 2025Reproducibility and Artifact Consistency of the SIGIR 2022 Recommender Systems Papers Based on Message Passingcited by 0
  38. 2025Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Datacited by 0

50 most cited of 53 All 53 (JSON)