mlsanityData to 14 Oct 2026

Graph Collaborative Signals Denoising and Augmentation for Recommendation

arXiv 2304.03344 ↗First posted 6 Apr 2023cs.IR cs.AI cs.LGcited by 16

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Abstract (arXiv)

Graph collaborative filtering (GCF) is a popular technique for capturing high-order collaborative signals in recommendation systems. However, GCF's bipartite adjacency matrix, which defines the neighbors being aggregated based on user-item interactions, can be noisy for users/items with abundant interactions and insufficient for users/items with scarce interactions. Additionally, the adjacency matrix ignores user-user and item-item correlations, which can limit the scope of beneficial neighbors being aggregated. In this work, we propose a new graph adjacency matrix that incorporates user-user and item-item correlations, as well as a properly designed user-item interaction matrix that balances the number of interactions across all users. To achieve this, we pre-train a graph-based recommendation method to obtain users/items embeddings, and then enhance the user-item interaction matrix via top-K sampling. We also augment the symmetric user-user and item-item correlation components to the adjacency matrix. Our experiments demonstrate that the enhanced user-item interaction matrix with improved neighbors and lower density leads to significant benefits in graph-based recommendation. Moreover, we show that the inclusion of user-user and item-item correlations can improve recommendations for users with both abundant and insufficient interactions. The code is in urlhttps://github.com/zfan20/GraphDA.

Key facts. Cited by 16 papers in the map (data through 14 Oct 2026). Builds on 18 of its 39 references that are in the map. First posted on arXiv 6 Apr 2023.

Builds on

18 of 39 references.

References · 18

  1. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,139
  2. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,138
  3. 2015Image-based Recommendations on Styles and Substitutescited by 706
  4. 2019Neural Graph Collaborative Filteringcited by 661
  5. 2019Measuring and Relieving the Over-smoothing Problem for Graph Neural Networks from the Topological Viewcited by 545
  6. 2016Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filteringcited by 520
  7. 2020Self-supervised Graph Learning for Recommendationcited by 305
  8. 2020Bias and Debias in Recommender System: A Survey and Future Directionscited by 240
  9. 2021Graph Condensation for Graph Neural Networkscited by 150
  10. 2021UltraGCN: Ultra Simplification of Graph Convolutional Networks for Recommendationcited by 87
  11. 2019The Unfairness of Popularity Bias in Recommendationcited by 70
  12. 2021Cold Brew: Distilling Graph Node Representations with Incomplete or Missing Neighborhoodscited by 58
6 more
  1. 2021Federated Social Recommendation with Graph Neural Networkcited by 56
  2. 2021Graph Trend Filtering Networks for Recommendationscited by 53
  3. 2020Pre-Training Graph Neural Networks for Cold-Start Users and Items Representationcited by 42
  4. 2021DSKReG: Differentiable Sampling on Knowledge Graph for Recommendation with Relational GNNcited by 19
  5. 2016A Generic Coordinate Descent Framework for Learning from Implicit Feedbackcited by 16
  6. 2022Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Networkcited by 14

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Citing papers per year: 2023: 3; 2024: 7; 2025: 6.

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  1. 2024Denoising Diffusion Recommender Modelcited by 28
  2. 2024Score-based Generative Diffusion Models for Social Recommendationscited by 23
  3. 2023Data Scarcity in Recommendation Systems: A Surveycited by 17
  4. 2024LLM4DSR: Leveraging Large Language Model for Denoising Sequential Recommendationcited by 10
  5. 2023Graph Neural Ordinary Differential Equations-based method for Collaborative Filteringcited by 5
  6. 2023Learning to Denoise Biomedical Knowledge Graph for Robust Molecular Interaction Predictioncited by 5
  7. 2025Recommendation Is a Dish Better Served Warmcited by 2
  8. 2024Fair Augmentation for Graph Collaborative Filteringcited by 1
  9. 2025Adaptive Long-term Embedding with Denoising and Augmentation for Recommendationcited by 1
  10. 2024Dual-Channel Latent Factor Analysis Enhanced Graph Contrastive Learning for Recommendationcited by 0
  11. 2024Enhancing Recommendation with Denoising Auxiliary Taskcited by 0
  12. 2024S2DN: Learning to Denoise Unconvincing Knowledge for Inductive Knowledge Graph Completioncited by 0
4 more
  1. 2025VoteGCL: Enhancing Graph-based Recommendations with Majority-Voting LLM-Rerank Augmentationcited by 0
  2. 2025EGRA:Toward Enhanced Behavior Graphs and Representation Alignment for Multimodal Recommendationcited by 0
  3. 2025Empowering Denoising Sequential Recommendation with Large Language Model Embeddingscited by 0
  4. 2025Denoised Recommendation Model with Collaborative Signal Decouplingcited by 0