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Reviewing Developments of Graph Convolutional Network Techniques for Recommendation Systems

arXiv 2311.06323 ↗First posted 10 Nov 2023cs.IR cs.AI cs.LGcited by 0

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Abstract (arXiv)

The Recommender system is a vital information service on today's Internet. Recently, graph neural networks have emerged as the leading approach for recommender systems. We try to review recent literature on graph neural network-based recommender systems, covering the background and development of both recommender systems and graph neural networks. Then categorizing recommender systems by their settings and graph neural networks by spectral and spatial models, we explore the motivation behind incorporating graph neural networks into recommender systems. We also analyze challenges and open problems in graph construction, embedding propagation and aggregation, and computation efficiency. This guides us to better explore the future directions and developments in this domain.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 55 of its 106 references that are in the map. First posted on arXiv 10 Nov 2023.

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References · 55

  1. 2017Graph Attention Networkscited by 8,854
  2. 2017Inductive Representation Learning on Large Graphscited by 6,284
  3. 2013Efficient Estimation of Word Representations in Vector Spacecited by 4,346
  4. 2019A Comprehensive Survey on Graph Neural Networkscited by 2,906
  5. 2019Fast Graph Representation Learning with PyTorch Geometriccited by 2,414
  6. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,571
  7. 2014DeepWalk: Online Learning of Social Representationscited by 1,543
  8. 2019Simplifying Graph Convolutional Networkscited by 1,470
  9. 2018Self-Attentive Sequential Recommendationcited by 1,169
  10. 2020LightGCN: Simplifying and Powering Graph Convolution Network for Recommendationcited by 1,134
  11. 2018Graph Convolutional Neural Networks for Web-Scale Recommender Systemscited by 1,133
  12. 2018Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learningcited by 1,099
38 more
  1. 2013Spectral Networks and Locally Connected Networks on Graphscited by 1,097
  2. 2018Predict then Propagate: Graph Neural Networks meet Personalized PageRankcited by 987
  3. 2016Wide & Deep Learning for Recommender Systemscited by 745
  4. 2017DeepFM: A Factorization-Machine based Neural Network for CTR Predictioncited by 673
  5. 2019Neural Graph Collaborative Filteringcited by 660
  6. 2018FastGCN: Fast Learning with Graph Convolutional Networks via Importance Samplingcited by 583
  7. 2020Graph Neural Networks in Recommender Systems: A Surveycited by 493
  8. 2019KGAT: Knowledge Graph Attention Network for Recommendationcited by 342
  9. 2018AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networkscited by 319
  10. 2018xDeepFM: Combining Explicit and Implicit Feature Interactions for Recommender Systemscited by 277
  11. 2018Explainable Recommendation: A Survey and New Perspectivescited by 210
  12. 2019AliGraph: A Comprehensive Graph Neural Network Platformcited by 152
  13. 2017Towards Accurate Binary Convolutional Neural Networkcited by 148
  14. 2018DKN: Deep Knowledge-Aware Network for News Recommendationcited by 137
  15. 2018RippleNet: Propagating User Preferences on the Knowledge Graph for Recommender Systemscited by 136
  16. 2017Heterogeneous Information Network Embedding for Recommendationcited by 133
  17. 2020Dynamic Multiscale Graph Neural Networks for 3D Skeleton-Based Human Motion Predictioncited by 132
  18. 2018DeepInf: Social Influence Prediction with Deep Learningcited by 128
  19. 2017HashNet: Deep Learning to Hash by Continuationcited by 121
  20. 2019Knowledge Graph Convolutional Networks for Recommender Systemscited by 121
  21. 2020FastIF: Scalable Influence Functions for Efficient Model Interpretation and Debuggingcited by 120
  22. 2020Revisiting Graph based Collaborative Filtering: A Linear Residual Graph Convolutional Network Approachcited by 119
  23. 2018Temporal Relational Ranking for Stock Predictioncited by 100
  24. 2018Streaming Graph Neural Networkscited by 91
  25. 2022Knowledge Graph Contrastive Learning for Recommendationcited by 69
  26. 2018NAIS: Neural Attentive Item Similarity Model for Recommendationcited by 52
  27. 2019Jointly Learning Explainable Rules for Recommendation with Knowledge Graphcited by 40
  28. 2020Learning to Embed Categorical Features without Embedding Tables for Recommendationcited by 38
  29. 2021Modeling Scale-free Graphs with Hyperbolic Geometry for Knowledge-aware Recommendationcited by 38
  30. 2019Deep Social Collaborative Filteringcited by 32
  31. 2018Deep Item-based Collaborative Filtering for Top-N Recommendationcited by 20
  32. 2019Candidate Generation with Binary Codes for Large-Scale Top-N Recommendationcited by 19
  33. 2018Globally Optimized Mutual Influence Aware Ranking in E-Commerce Searchcited by 18
  34. 2017BiRank: Towards Ranking on Bipartite Graphscited by 15
  35. 2020Alleviating Cold-Start Problems in Recommendation through Pseudo-Labelling over Knowledge Graphcited by 12
  36. 2021Attentive Knowledge-aware Graph Convolutional Networks with Collaborative Guidance for Personalized Recommendationcited by 12
  37. 2017Item Silk Road: Recommending Items from Information Domains to Social Userscited by 11
  38. 2022Learning Binarized Graph Representations with Multi-faceted Quantization Reinforcement for Top-K Recommendationcited by 10

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