mlsanityData to 7 Oct 2026

Neural Graph Matching based Collaborative Filtering

arXiv 2105.04067 ↗First posted 10 May 2021cs.IR cs.AIcited by 9

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Abstract (arXiv)

User and item attributes are essential side-information; their interactions (i.e., their co-occurrence in the sample data) can significantly enhance prediction accuracy in various recommender systems. We identify two different types of attribute interactions, inner interactions and cross interactions: inner interactions are those between only user attributes or those between only item attributes; cross interactions are those between user attributes and item attributes. Existing models do not distinguish these two types of attribute interactions, which may not be the most effective way to exploit the information carried by the interactions. To address this drawback, we propose a neural Graph Matching based Collaborative Filtering model (GMCF), which effectively captures the two types of attribute interactions through modeling and aggregating attribute interactions in a graph matching structure for recommendation. In our model, the two essential recommendation procedures, characteristic learning and preference matching, are explicitly conducted through graph learning (based on inner interactions) and node matching (based on cross interactions), respectively. Experimental results show that our model outperforms state-of-the-art models. Further studies verify the effectiveness of GMCF in improving the accuracy of recommendation.

Key facts. Cited by 9 papers in the map (data through 7 Oct 2026). Builds on 27 of its 47 references that are in the map. First posted on arXiv 10 May 2021.

Builds on

27 of 47 references.

References · 27

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  8. 2019Neural Graph Collaborative Filteringcited by 660
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  11. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
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15 more
  1. 2019Graph Matching Networks for Learning the Similarity of Graph Structured Objectscited by 202
  2. 2017Neural Factorization Machines for Sparse Predictive Analyticscited by 183
  3. 2017Attentional Factorization Machines: Learning the Weight of Feature Interactions via Attention Networkscited by 126
  4. 2016A Compositional Object-Based Approach to Learning Physical Dynamicscited by 119
  5. 2019Knowledge-aware Graph Neural Networks with Label Smoothness Regularization for Recommender Systemscited by 107
  6. 2019Reinforcement Knowledge Graph Reasoning for Explainable Recommendationcited by 102
  7. 2018SimGNN: A Neural Network Approach to Fast Graph Similarity Computationcited by 89
  8. 2019Fi-GNN: Modeling Feature Interactions via Graph Neural Networks for CTR Predictioncited by 54
  9. 2020AutoFIS: Automatic Feature Interaction Selection in Factorization Models for Click-Through Rate Predictioncited by 52
  10. 2020Detecting Beneficial Feature Interactions for Recommender Systemscited by 22
  11. 2021Graph Pooling via Coarsened Graph Infomaxcited by 15
  12. 2021WGCN: Graph Convolutional Networks with Weighted Structural Featurescited by 12
  13. 2014Incorporating Side Information in Probabilistic Matrix Factorization with Gaussian Processescited by 4
  14. 2019MMF: Attribute Interpretable Collaborative Filteringcited by 2
  15. 2019CARL: Aggregated Search with Context-Aware Module Embedding Learningcited by 1

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Cited by year

Citing papers per year: 2021: 1; 2022: 3; 2023: 2; 2024: 1; 2025: 2.

9 papers.

  1. 2021Memorize, Factorize, or be Na"ive: Learning Optimal Feature Interaction Methods for CTR Predictioncited by 12
  2. 2022CGMN: A Contrastive Graph Matching Network for Self-Supervised Graph Similarity Learningcited by 8
  3. 2023Multi-domain Recommendation with Embedding Disentangling and Domain Alignmentcited by 7
  4. 2022Detecting Arbitrary Order Beneficial Feature Interactions for Recommender Systemscited by 6
  5. 2025Learning without Isolation: Pathway Protection for Continual Learningcited by 6
  6. 2024A Survey on Deep Neural Networks in Collaborative Filtering Recommendation Systemscited by 3
  7. 2022Privacy-Preserved Neural Graph Similarity Learningcited by 2
  8. 2023Ripple Knowledge Graph Convolutional Networks For Recommendation Systemscited by 2
  9. 2025Intrinsic and Extrinsic Factor Disentanglement for Recommendation in Various Context Scenarioscited by 0