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GCN recommendation model based on the fusion of dynamic multiple-view latent interest topics

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Abstract

Graph neural network-based representation models have shown extraordinary potential in numerous recommender system applications. Previous studies mainly considered high-order connectivity information in a single view from an interaction graph but ignored the individualized information of the users or items, making them vulnerable to over-smoothing problems. In this study, we proposed a dynamic multi-view fusion-based graph convolution network model for recommendation systems. Multiple views were generated for learning on the basis of latent user interest topics from the decomposed matrix, and a continuous awareness mechanism was proposed to maintain the model’s focus on the individualized features of the nodes. During the graph learning process, a dynamic aggregation mechanism was designed to adjust the fusion weight of different propagation layers. Lastly, the different features from multiple views were dynamically fused through an attention mechanism and a principal component control mechanism to predict the similarities between users and items. Experimental results of three popular datasets of recommendation systems demonstrated that our method could effectively alleviate the over-smoothing problem and achieved better performance than four state-of-the-art baselines.

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Notes

  1. https://movielens.org/.

  2. https://www.last.fm.

  3. https://github.com/gusye1234/LightGCN-PyTorch/tree/master/data/LastFM.

  4. https://pytorch.org/.

References

  1. Abdollahi B, Nasraoui O (2016) Explainable matrix factorization for collaborative filtering. In: Proceedings of the 25th International Conference Companion on World Wide Web, Republic and Canton of Geneva, CHE, pp 5–6

  2. Chanpuriya S, Musco C (2020) Infinitewalk: Deep network embeddings as Laplacian embeddings with a nonlinearity. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’20, pp 1325–1333

  3. Chen CM, Wang CJ, Tsai MF et al (2019a) Collaborative similarity embedding for recommender systems. In: Proceedings of the International Conference of World Wide Web, pp 2637–2643

  4. Chen L, Wu L, Hong R et al (2020a) Revisiting graph based collaborative filtering: a linear residual graph convolutional network approach. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 27–34

  5. Chen M, Wei Z, Huang Z et al (2020b) Simple and deep graph convolutional networks. In: Proceedings of the International Conference on Machine Learning, pp 1725–1735

  6. Chen W, Cai F, Chen H et al (2019) Joint neural collaborative filtering for recommender systems. ACM Trans Inf Syst 37(4):1–30

    Article  Google Scholar 

  7. Chen X, Li L, Pan W et al (2020) A survey on heterogeneous one-class collaborative filtering. ACM Trans Inf Syst 38(4):1–54

    Google Scholar 

  8. Chen YW, Song Q, Hu X (2021) Techniques for automated machine learning. ACM SIGKDD Explor Newsl (SIGKDD) 22(2):35–50

    Article  Google Scholar 

  9. Christakis NA, Fowler JH (2009) Connected: The surprising power of our social networks and how they shape our lives. Little, Brown Spark, New York

    Google Scholar 

  10. Cui Q, Wu S, Liu Q et al (2020) Mv-rnn: a multi-view recurrent neural network for sequential recommendation. IEEE Trans Knowl Data Eng 32(2):317–331

    Article  Google Scholar 

  11. Du X, He X, Yuan F et al (2019) Modeling embedding dimension correlations via convolutional neural collaborative filtering. ACM Trans Inf Syst 37(4):1–22

    Article  Google Scholar 

  12. Fan L, Zhiyong C, Lei Z et al (2020) \(a^2\)-gcn: An attribute-aware attentive gcn model for recommendation. IEEE Trans Knowl Data Eng, pp 1–11

  13. Grover A, Leskovec J (2016) Node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’16, pp 855–864

  14. He X, Liao L, Zhang H et al (2017) Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp 173–182

  15. Hu G, Zhang Y, Yang Q (2019a) Transfer meets hybrid: a synthetic approach for cross-domain collaborative filtering with text. In: Proceedings of the International Conference of World Wide Web. Association for Computing Machinery, New York, NY, USA, pp 2822–2829

  16. Hu Q, Han Z, Lin X et al (2019) Learning peer recommendation using attention-driven cnn with interaction tripartite graph. Inf Sci 479:231–249

    Article  MathSciNet  Google Scholar 

  17. Klicpera J, Bojchevski A, Günnemann S (2019) Predict then propagate: graph neural networks meet personalized pagerank. In: Proceedings of the International Conference on Learning Representations

  18. Li Q, Han Z, Wu XM (2018) Deeper insights into graph convolutional networks for semi-supervised learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp 3538–3545

  19. Liu F, Cheng Z, Sun C et al (2019) User diverse preference modeling by multimodal attentive metric learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp 1526–1534

  20. Liu F, Cheng Z, Zhu L et al (2021) Interest-aware message-passing gcn for recommendation. In: Proceedings of the International Conference of World Wide Web, pp 1296–1305

  21. Liu W, Wang Z, Liu X et al (2017) A survey of deep neural network architectures and their applications. Neurocomputing 234:11–26

    Article  Google Scholar 

  22. Mao K, Zhu J, Xiao X et al (2021) Ultragcn - ultra simplification of graph convolutional networks for recommendation. In: Proceedings of the ACM International Conference on Information and Knowledge Management, pp 1253–1262

  23. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’14, pp 701–710

  24. Qiu J, Dong Y, Ma H, et al (2018) Network embedding as matrix factorization: Unifying deepwalk, line, pte, and node2vec. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, New York, NY, USA, WSDM ’18, pp 459–467

  25. Shi M, Tang Y, Liu J (2019) Functional and contextual attention-based lstm for service recommendation in mashup creation. IEEE Trans Parallel Distrib Syst 30(5):1077–1090

    Article  Google Scholar 

  26. Wang X, He X, Wang M et al (2019) Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, SIGIR’19, pp 165–174

  27. Wang X, Jin H, Zhang A et al (2020) Disentangled graph collaborative filtering. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp 1001–1010

  28. Watts DJ, Strogatz SH (1998) Collective dynamics of small-world networks. Nature 393(6684):440–442

    Article  MATH  Google Scholar 

  29. Wei Y, Wang X, Nie L et al (2020) Graph-refined convolutional network for multimedia recommendation with implicit feedback. In: Proceedings of the 28th ACM International Conference on Multimedia, pp 3541–3549

  30. Wu F, Souza HA, Zhang T et al (2019) Simplifying graph convolutional networks. In: Proceedings of the International Conference on Machine Learning, pp 6861–6871

  31. Wu J, Wang X, Feng F et al (2021) Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, pp 726–735

  32. Xiangnan H, Kuan D, Xiang W et al (2020) Lightgcn: simplifying and powering graph convolution network for recommendation. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, pp 639–648

  33. Xiao T, Chen Z, Wang D et al (2021) Learning how to propagate messages in graph neural networks. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’21, pp 1894–1903

  34. Xu K, Hu W, Leskovec J et al (2019) How powerful are graph neural networks? In: Proceedings of the International Conference on Learning Representations

  35. Yao Q, Chen X, Kwok JT et al (2020) Efficient neural interaction function search for collaborative filtering. New York, NY, USA, pp 1660–1670

  36. Yu W, Lin X, Ge J et al (2020) Semi-supervised collaborative filtering by text-enhanced domain adaptation. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, KDD ’20, pp 2136–2144

  37. Zhang H, Shen F, Liu W et al (2016) Discrete collaborative filtering. In: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, New York, NY, USA, SIGIR ’16, pp 325–334

  38. Zheng J, Li Q, Liao J (2021) Heterogeneous type-specific entity representation learning for recommendations in e-commerce network. Inf Process Manag 58(5):102629

    Article  Google Scholar 

  39. Zheng J, Li Q, Liao J et al (2021) Explainable link prediction based on multi-granularity relation-embedded representation. Knowl-Based Syst 230(15):107402

    Article  Google Scholar 

  40. Zheng L, Lu CT, Jiang F et al (2018) Spectral collaborative filtering. In: Proceedings of the 12th ACM Conference on Recommender Systems, New York, NY, USA, RecSys ’18, pp 311–319

  41. Zhou Y, Huang C, Hu Q et al (2018) Personalized learning full-path recommendation model based on lstm neural networks. Inf Sci 444:135–152

    Article  Google Scholar 

  42. Zou L, Xia L, Gu Y et al (2020) Neural interactive collaborative filtering. New York, NY, USA, pp 749–758

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Acknowledgements

The authors would like to thank all anonymous reviewers for their valuable comments and suggestions which have significantly improved the quality and presentation of this paper. The works described in this paper are supported by the National Natural Science Foundation of China (61906112, 62076158, 62272286), Natural Science Foundation of Shanxi Province, China (201901D211174, 20210302123468), Scientific and Technological Innovation Programs of Higher Education Institutions in Shanxi Province, China (2019L0008).

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Author notes

  1. Feng Liu and Jian Liao have contributed equally to this work.

Authors and Affiliations

  1. School of Computer and Information Technology, Shanxi University, Taiyuan, China

    Feng Liu, Jian Liao, Suge Wang & Deyu Li

  2. Institute of Intelligent Information Processing, Shanxi University, Taiyuan, China

    Jianxing Zheng

  3. Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education, Shanxi University, Taiyuan, China

    Suge Wang & Deyu Li

  4. Shanxi Yihong Gas Industry Co., Ltd, Taiyuan, China

    Xin Wang

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  1. Feng Liu
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  2. Jian Liao
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  5. Deyu Li
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Correspondence to Jian Liao.

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Liu, F., Liao, J., Zheng, J. et al. GCN recommendation model based on the fusion of dynamic multiple-view latent interest topics. Int. J. Mach. Learn. & Cyber. 14, 2023–2039 (2023). https://doi.org/10.1007/s13042-022-01743-z

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