Skip to main content
SpringerLink
Log in

MRAN: a attention-based approach for social recommendation

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

Graph Neural Networks have been widely used in social recommendation systems. However, with the increase of graph nodes and diffusion depth, they tend to suffer from graph sparsity and over-smoothing, which inhibit their performance. In this work, we propose the multi-relational attention network, named as MRAN, for social recommendation. Our model has three distinctive characteristics: (i) it alleviates the data sparsity problem in social recommendation scenarios by incorporating both user social relations and item homogeneous relations as supplementary information; (ii) it mimics the structure of influence diffusion in user and item domain via an iteratively aggregating structure; (iii) it has a two-level attention mechanism at the diffusion and aggregating level, enabling it to differentiate importance of embeddings to overcome the over-smoothing problem. Experiments conducted on two large-scale representative datasets demonstrate that the proposed model outperforms previous methods substantially. The ablation study shows that the performance of MRAN can be further improved avoid over-smoothing by increasing the diffusion depth.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2

Similar content being viewed by others

Data Availibility

Not applicable.

References

  1. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. https://doi.org/10.1145/3038912.3052569

  2. Wang X, He X, Wang M, Feng F, Chua T-S (2019) Neural graph collaborative filtering, pp 165–174. https://doi.org/10.1145/3331184.3331267

  3. Zhang J, Shi X, Zhao S, King I (2019) Star-gcn: stacked and reconstructed graph convolutional networks for recommender systems, pp 4264–4270. https://doi.org/10.24963/ijcai.2019/592

  4. Su X, Khoshgoftaar T (2009) A survey of collaborative filtering techniques. Adv Artif Intell. https://doi.org/10.1155/2009/421425

    Article  Google Scholar 

  5. Herlocker J, Konstan J, Borchers A, Riedl J (1999) An algorithmic framework for performing collaborative filtering, pp 230–237. https://doi.org/10.1145/312624.312682

  6. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. In: Proceedings of ACM World Wide Web Conference, vol 1. https://doi.org/10.1145/371920.372071

  7. Do M-P, Nguyen D, Nguyen L (2010) Model-based approach for collaborative filtering

  8. Jenatton R, Roux N, Bordes A, Obozinski G (2012) A latent factor model for highly multi-relational data. vol 4

  9. Lü L, Medo M, Yeung CH, Zhang Y-C, Zhang Z-K, Zhou T (2012) Recommender systems. Phys Rep 519(1):1–49. https://doi.org/10.1016/j.physrep.2012.02.006 (Recommender Systems)

    Article  Google Scholar 

  10. Rendle S (2010) Factorization machines. In: 2010 IEEE International Conference on Data Mining, pp 995–1000. https://doi.org/10.1109/ICDM.2010.127

  11. Rendle S (2012) Factorization machines with libfm. ACM Trans Intell Syst Technol (TIST). https://doi.org/10.1145/2168752.2168771

    Article  Google Scholar 

  12. Rendle S, Freudenthaler C, Gantner Z, Schmidt-Thieme L (2009) Bpr: bayesian personalized ranking from implicit feedback, pp 452–461

  13. Zhou X, He J, Huang G, Zhang Y (2014) Svd-based incremental approaches for recommender systems. J Comput Syst Sci. https://doi.org/10.1016/j.jcss.2014.11.016

    Article  MATH  Google Scholar 

  14. Koren Y, Bell R, Volinsky C (2009) Matrix factorization techniques for recommender systems. Computer 42:30–37

    Article  Google Scholar 

  15. Sarwar B, Karypis G, Konstan J, Riedl J (2000) Application of dimensionality reduction in recommender system–a case study

  16. Fan M (2012) Non-negative matrix factorization and clustering methods application research in personalized recommendation system

  17. Wang X, Lu W, Ester M, Wang C, Chen C (2016) Social recommendation with strong and weak ties, pp 5–14. https://doi.org/10.1145/2983323.2983701

  18. Tang J, Hu X, Liu H (2013) Social recommendation: a review. Soc Netw Anal Min 3:1113–1133. https://doi.org/10.1007/s13278-013-0141-9

    Article  Google Scholar 

  19. Ma H, Yang H, Lyu M, King I (2008) Sorec: Social recommendation using probabilistic matrix factorization, pp 931–940. https://doi.org/10.1145/1458082.1458205

  20. Yang B, Lei Y, Liu J, Li W (2016) Social collaborative filtering by trust. IEEE Trans Pattern Anal Mach Intell. https://doi.org/10.1109/TPAMI.2016.2605085

    Article  Google Scholar 

  21. Fang H, Bao Y, Zhang J (2014) Leveraging decomposed trust in probabilistic matrix factorization for effective recommendation. Procee Nat Conf Artif Intell 1:30–36

    Google Scholar 

  22. Tang J, Hu X, Gao H, Liu H (2013) Exploiting local and global social context for recommendation. In: IJCAI International Joint Conference on Artificial Intelligence, pp 2712–2718

  23. Koren Y (2008) Factorization meets the neighborhood: a multifaceted collaborative filtering model, pp 426–434. https://doi.org/10.1145/1401890.1401944

  24. Guo G, Zhang J, Yorke-Smith N (2015) Trustsvd: collaborative filtering with both the explicit and implicit influence of user trust and of item ratings. Procee Twenty-Ninth AAAI Conf Artif Intell 29:123–129. https://doi.org/10.1609/aaai.v29i1.9153

    Article  Google Scholar 

  25. Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M (2020) Graph neural networks: a review of methods and applications. AI Open 1:57–81. https://doi.org/10.1016/j.aiopen.2021.01.001

    Article  Google Scholar 

  26. Kipf T, Welling M (2016) Semi-supervised classification with graph convolutional networks

  27. Wu N, Wang C (2022) Ensemble graph attention networks. Trans Mach Learn Artif Intell 10, 29–41. https://doi.org/10.14738/tmlai.103.12399

  28. Hamilton W, Ying R, Leskovec J (2017) Inductive representation learning on large graphs

  29. Berg R, Kipf T, Welling M (2017) Graph convolutional matrix completion

  30. Ying R, He R, Chen K, Eksombatchai P, Hamilton W, Leskovec J (2018). Graph convolutional neural networks for web-scale recommender systems. https://doi.org/10.1145/3219819.3219890

  31. Fan W, Ma Y, Li Q, He Y, Zhao E, Tang J, Yin D (2019) Graph neural networks for social recommendation, pp 417–426. https://doi.org/10.1145/3308558.3313488

  32. Wu L, Sun P, Fu Y, Hong R, Wang X, Wang M (2019) A neural influence diffusion model for social recommendation, pp 235–244. https://doi.org/10.1145/3331184.3331214

  33. Wu L, Li J, Sun P, Hong R, Ge Y, Wang M (2020) Diffnet++: a neural influence and interest diffusion network for social recommendation. IEEE Trans Knowl Data Eng. https://doi.org/10.1109/TKDE.2020.3048414

    Article  Google Scholar 

  34. Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation algorithms. vol 1. https://doi.org/10.1145/371920.372071

  35. Wu Q, Zhang H, Gao X, He P, Weng P, Gao H, Chen G (2019) Dual graph attention networks for deep latent representation of multifaceted social effects in recommender systems. https://doi.org/10.1145/3308558.3313442

  36. Zhao T, McAuley J, King I (2014) Leveraging social connections to improve personalized ranking for collaborative filtering, pp 261–270. https://doi.org/10.1145/2661829.2661998

  37. Chen J, Zhang H, He X, Nie L, Liu W, Chua T-S (2017) Attentive collaborative filtering: multimedia recommendation with item- and component-level attention, pp 335–344. https://doi.org/10.1145/3077136.3080797

  38. You D, Vo N, Lee K, Liu Q (2020) Attributed multi-relational attention network for fact-checking url recommendation

  39. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering, pp 173–182. https://doi.org/10.1145/3038912.3052569

  40. Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks, pp 135–142. https://doi.org/10.1145/1864708.1864736

  41. Jiang M, Cui P, Wang F, Zhu W, Yang S (2014) Scalable recommendation with social contextual information. Knowl Data Eng IEEE Trans 26:2789–2802. https://doi.org/10.1109/TKDE.2014.2300487

    Article  Google Scholar 

Download references

Acknowledgements

This work is partly supported by a grant from the Innovative Research Foundation of Ship General Performance (14422102).

Funding

This work is partly supported by a grant from the Innovative Research Foundation of Ship General Performance (14422102).

Author information

Authors and Affiliations

  1. School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University, 800 Dongchuan RD. Minhang District, Shanghai, 200240, Shanghai, China

    Yiyang Fu

  2. School of Artificial Intelligence and Computer, Jiangnan University, No. 1800, Lihu Avenue, Wuxi, 214122, Jiangsu, China

    Xiaojun Xie & Tao Zhang

  3. China Ship Scientific Research Center, No. 222, Shanshui East Road, Binhu District, Wuxi, 214122, Jiangsu, China

    Tao Zhang

Authors
  1. Yiyang Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xiaojun Xie
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Tao Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

YF: Conceptualization, Methodology, Software, Writing Original draft preparation. XX: Investigation, Experiment, Writing Reviewing and Editing. TZ: Supervision, Writing Reviewing and Editing. All authors reviewed the manuscript.

Corresponding author

Correspondence to Yiyang Fu.

Ethics declarations

Conflict of interest

The authors declare that they have no competing interests.

Ethical Approval

Not applicable.

Consent for publication

Not applicable.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fu, Y., Xie, X. & Zhang, T. MRAN: a attention-based approach for social recommendation. J Supercomput 79, 8295–8321 (2023). https://doi.org/10.1007/s11227-022-04985-4

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-022-04985-4

Keywords

Search

Navigation