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Incorporating metapath interaction on heterogeneous information network for social recommendation

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Abstract

Heterogeneous information network (HIN) has recently been widely adopted to describe complex graph structure in recommendation systems, proving its effectiveness in modeling complex graph data. Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths, they have the following major limitations. Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths, which are not expressive enough to capture more complicated dependency relationships involved on the metapath. Besides, the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered. To tackle these limitations, we propose a novel social recommendation model MPISR, which models MetaPath Interaction for Social Recommendation on heterogeneous information network. Specifically, our model first learns the initial node representation through a pretraining module, and then identifies potential social friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Extensive experiments on five real datasets demonstrate the effectiveness of our method.

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References

  1. Jiang Y, Ma H, Liu Y, Li Z, Chang L. Enhancing social recommendation via two-level graph attentional networks. Neurocomputing, 2021, 449: 71–84

    Google Scholar 

  2. Qian X, Feng H, Zhao G, Mei T. Personalized recommendation combining user interest and social circle. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(7): 1763–1777

    MATH  Google Scholar 

  3. Jamali M, Ester M. A matrix factorization technique with trust propagation for recommendation in social networks. In: Proceedings of the 4th ACM Conference on Recommender Systems. 2010, 135–142

  4. Jiang M, Cui P, Wang F, Zhu W, Yang S. Scalable recommendation with social contextual information. IEEE Transactions on Knowledge and Data Engineering, 2014, 26(11): 2789–2802

    MATH  Google Scholar 

  5. Song C, Wang B, Jiang Q, Zhang Y, He R, Hou Y. Social recommendation with implicit social influence. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2021, 1788–1792

  6. Wang X, He X, Cao Y, Liu M, Chua T S. KGAT: knowledge graph attention network for recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 950–958

  7. Wang H, Zhao M, Xie X, Li W, Guo M. Knowledge graph convolutional networks for recommender systems. In: Proceedings of the World Wide Web Conference. 2019, 3307–3313

  8. Chang L, Chen W, Huang J, Bin C, Wang W. Exploiting multi-attention network with contextual influence for point-of-interest recommendation. Applied Intelligence, 2021, 51(4): 1904–1917

    MATH  Google Scholar 

  9. Wang X, He X, Wang M, Feng F, Chua T S. Neural graph collaborative filtering. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval. 2019, 165–174

  10. Kipf T N, Welling M. Semi-supervised classification with graph convolutional networks. In: Proceedings of the 5th International Conference on Learning Representations. 2017

  11. Zheng Y, Gao C, He X, Li Y, Jin D. Price-aware recommendation with graph convolutional networks. In: Proceedings of the 36th IEEE International Conference on Data Engineering. 2020, 133–144

  12. Wang X, Ji H, Shi C, Wang B, Ye Y, Cui P, Yu P S. Heterogeneous graph attention network. In: Proceedings of the World Wide Web Conference. 2019, 2022–2032

  13. Shi C, Yu P S. Recent developments of deep heterogeneous information network analysis. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management. 2019, 2973–2974

  14. Yu X, Ren X, Sun Y, Sturt S, Khandelwal U, Gu Q, Norick B, Han J. Recommendation in heterogeneous information networks with implicit user feedback. In: Proceedings of the 7th ACM Conference on Recommender Systems. 2013, 347–350

  15. Zhao H, Yao Q, Li J, Song Y, Lee D L. Meta-graph based recommendation fusion over heterogeneous information networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017, 635–644

  16. Hu B, Shi C, Zhao W X, Yang T. Local and global information fusion for top-n recommendation in heterogeneous information network. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 1683–1686

  17. Shi C, Hu B, Zhao W X, Yu P S. Heterogeneous information network embedding for recommendation. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(2): 357–370

    MATH  Google Scholar 

  18. Jiang Z, Liu Z, Fu B, Wu Z, Zhang T. Recommendation in heterogeneous information networks based on generalized random walk model and Bayesian personalized ranking. In: Proceedings of the 11th ACM International Conference on Web Search and Data Mining. 2018, 288–296

  19. Fan S, Zhu J, Han X, Shi C, Hu L, Ma B, Li Y. Metapath-guided heterogeneous graph neural network for intent recommendation. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 2478–2486

  20. Yu X, Ren X, Sun Y, Gu Q, Sturt B, Khandelwal U, Norick B, Han J. Personalized entity recommendation: a heterogeneous information network approach. In: Proceedings of the 7th ACM International Conference on Web Search and Data Mining. 2014, 283–292

  21. Zhang C, Song D, Huang C, Swami A, Chawla N V. Heterogeneous graph neural network. In: Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2019, 793–803

  22. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L. BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence. 2009, 452–461

  23. Xue H J, Dai X Y, Zhang J, Huang S, Chen J. Deep matrix factorization models for recommender systems. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence. 2017, 3203–3209

  24. Rendle S. Factorization machines. In: Proceedings of 2010 IEEE International Conference on Data Mining. 2010, 995–1000

  25. Rendle S. Factorization machines with libFM. ACM Transactions on Intelligent Systems and Technology, 2012, 3(3): 57

    Google Scholar 

  26. Van Den Berg R, Kipf T N, Welling M. Graph convolutional matrix completion. 2017, arXiv preprint arXiv: 1706.02263

  27. He X, Deng K, Wang X, Li Y, Zhang Y, Wang M. 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. 2020, 639–648

  28. Wu J, He X, Wang X, Wang F, Chen W, Lian J, Xie X. Graph convolution machine for context-aware recommender system. Frontiers of Computer Science, 2022, 16(6): 166614

    MATH  Google Scholar 

  29. Dong Y, Chawla N V, Swami A. metapath2vec: scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 2017, 135–144

  30. Fu T Y, Lee W C, Lei Z. Hin2Vec: explore meta-paths in heterogeneous information networks for representation learning. In: Proceedings of 2017 ACM on Conference on Information and Knowledge Management. 2017, 1797–1806

  31. Jin J, Qin J, Fang Y, Du K, Zhang W, Yu Y, Zhang Z, Smola A J. An efficient neighborhood-based interaction model for recommendation on heterogeneous graph. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2020, 75–84

  32. Fu X, Zhang J, Meng Z, King I. MAGNN: Metapath aggregated graph neural network for heterogeneous graph embedding. In: Proceedings of the Web Conference 2020. 2020, 2331–2341

  33. Nie L, Song X, Chua T S. Learning from Multiple Social Networks. Cham: Springer, 2016, 1–118

    MATH  Google Scholar 

  34. Yin H, Zhou X, Cui B, Wang H, Zheng K, Nguyen Q V H. Adapting to user interest drift for poi recommendation. IEEE Transactions on Knowledge and Data Engineering, 2016, 28(10): 2566–2581

    MATH  Google Scholar 

  35. Yu J, Gao M, Li J, Yin H, Liu H. Adaptive implicit friends identification over heterogeneous network for social recommendation. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 357–366

  36. Guan W, Song X, Gan T, Lin J, Chang X, Nie L. Cooperation learning from multiple social networks: consistent and complementary perspectives. IEEE Transactions on Cybernetics, 2021, 51(9): 4501–4514

    MATH  Google Scholar 

  37. Wang X, He X, Nie L, Chua T S. Item silk road: recommending items from information domains to social users. In: Proceedings of the 40th International ACM SIGIR Conference on Research and Development in Information Retrieval. 2017, 185–194

  38. Lin T H, Gao C, Li Y. Recommender systems with characterized social regularization. In: Proceedings of the 27th ACM International Conference on Information and Knowledge Management. 2018, 1767–1770

  39. Chen J, Wang C, Zhou S, Shi Q, Feng Y, Chen C. Samwalker: social recommendation with informative sampling strategy. In: Proceedings of the World Wide Web Conference. 2019, 228–239

  40. Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D. Graph neural networks for social recommendation. In: Proceedings of the World Wide Web Conference. 2019, 417–426

  41. Wu L, Li J, Sun P, Hong R, Ge Y, Wang M. Diffnet++: a neural influence and interest diffusion network for social recommendation. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(10): 4753–4766

    MATH  Google Scholar 

  42. Zhang C, Wang Y, Zhu L, Song J, Yin H. Multi-graph heterogeneous interaction fusion for social recommendation. ACM Transactions on Information Systems, 2022, 40(2): 28

    MATH  Google Scholar 

  43. Wu Q, Zhang H, Gao X, He P, Weng P, Gao H, Chen G. Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. In: Proceedings of the World Wide Web Conference. 2019, 2091–2102

  44. Sun Y, Han J, Yan X, Yu P S, Wu T. PathSim: meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment, 2011, 4(11): 992–1003

    MATH  Google Scholar 

  45. Zhang C, Yu L, Wang Y, Shah C, Zhang X. Collaborative user network embedding for social recommender systems. In: Proceedings of 2017 SIAM International Conference on Data Mining. 2017, 381–389

  46. Ying R, He R, Chen K, Eksombatchai P, Hamilton W L, Leskovec J. Graph convolutional neural networks for web-scale recommender systems. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. 2018, 974–983

  47. Hamilton W L, Ying R, Leskovec J. Inductive representation learning on large graphs. In: Proceedings of the 31st International Conference on Neural Information Processing Systems. 2017, 1025–1035

  48. Veličković P, Cucurull G, Casanova A, Romero A, Liò P, Bengio Y. Graph attention networks. 2017, arXiv preprint arXiv:1710, 1090: 3

  49. Guo G, Zhang J, Yorke-Smith N. TrustSVD: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence. 2015, 123–125

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Acknowledgements

This study was supported by the National Natural Science Foundation of China (Grant Nos. 61762078, 62276073, 61966009 and U22A2099), the Industrial Support Project of Gansu Colleges (No. 2022CYZC11), the Natural Science Foundation of Gansu Province (21JR7RA114), the Northwest Normal University Young Teachers Research Capacity Promotion Plan (NWNU-LKQN2019-2), the Industrial Support Project of Gansu Colleges (No. 2022CYZC11), and the Northwest Normal University Post-graduate Research Funding Project (2021KYZZ02107).

Author information

Authors and Affiliations

  1. College of Computer Science and Engineering, Northwest Normal University, Lanzhou, 730070, China

    Yanbin Jiang, Huifang Ma & Xiaohui Zhang

  2. Guangxi Key Lab of Multi-source Information Mining and Security, Guangxi Normal University, Guilin, 541001, China

    Huifang Ma & Zhixin Li

  3. Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, 541004, China

    Liang Chang

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  1. Yanbin Jiang
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  2. Huifang Ma
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Corresponding author

Correspondence to Huifang Ma.

Additional information

Yanbin Jiang is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. His general area of research is social recommendation and graph neural networks.

Huifang Ma is currently a professor in the College of Computer Science and Engineering at Northwest Normal University, China. She received the BE degree from Northwest Normal University, China in 2003 and the MS degree from Beijing Normal University, China in 2006. She received the PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. Her general area of research is data mining and machine learning.

Xiaohui Zhang is currently a postgraduate student in the College of Computer Science and Engineering at Northwest Normal University, China. Her general area of research is sequential recommendation and graph neural networks.

Zhixin Li is currently a professor in the College of Computer Science & Information Engineering & College of Software, Guangxi Normal University, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2010. His general area of research is natural language processing, machine learning, intelligent recommendation system and formal methods.

Liang Chang is a professor in the School of Computer Science and Information Security, Guilin University of Electronic Technology, China. He received his PhD degree from Institute of Computing Technology, Chinese Academy of Sciences, China in 2008. His research interest covers data and knowledge engineering, intelligent recommendation system, and formal methods.

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Jiang, Y., Ma, H., Zhang, X. et al. Incorporating metapath interaction on heterogeneous information network for social recommendation. Front. Comput. Sci. 18, 181302 (2024). https://doi.org/10.1007/s11704-022-2438-1

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