Skip to main content
SpringerLink
Log in

Leveraging the fine-grained user preferences with graph neural networks for recommendation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

With the explosion of information, recommendation systems have become important for users to find their interested information. Existing recommendation methods mainly utilize user historical interaction with items or user ratings to capture user past preferences. However, there is ignorance of various personalized reasons for users preferring an item, in which the reasons always dominate users’ preference strengths on the item. In addition, the linear nature of traditional recommendation methods makes them less effective in dealing with complex data. With the development of deep learning methods, graph neural networks provide an unprecedented opportunity for recommendations, since the user-item interactions can be naturally represented as a graph and the method can extract high-order complex relationships between users and items. In this paper, we propose a novel method leveraging the FIne-Grained user preferences with Graph Neural Networks (FigGNN) for recommendation to tackle these issues. More specifically, user-item interactions with user annotated tags and user ratings are constructed as a graph. In the process of graph message propagation, the user annotated tags are incorporated for understanding user preference reasons on items, and heterogeneous user rating levels are utilized for recognizing user preference strengths on items. Experiments have been conducted on the MovieLens dataset and the results show a superior performance of FigGNN over baselines in terms of precision and recall, which demonstrates the effectiveness of the proposed method.

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

Figure 1
Figure 2
Figure 3
Figure 4
Figure 5
Figure 6
Figure 7
Figure 8

Similar content being viewed by others

Data availability

The data used in the paper can be available at https://grouplens.org/datasets/movielens/.

References

  1. Fan, W., Ma, Y., Li, Q., Wang, J., Cai, G., Tang, J., et al.: A graph neural network framework for social recommendations[J]. IEEE Trans. Knowl. Data Eng. 34(5), 2033–2047 (2022)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the state-of-the-art and possible extensions[J]. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Bobadilla, J., Ortega, F., Hernando, A., Gutiérrez, A.: Recommender systems survey[J]. Knowl.-Based Syst. 46(1), 109–132 (2013)

    Article  Google Scholar 

  4. Zhang, S., Yao, L., Sun, A., Tay, Y.: Deep learning based recommender system: A survey and new perspectives[J]. ACM Comput. Surv. 52(1), 1–38 (2019)

    Article  Google Scholar 

  5. Wei, J., He, J., Chen, K., Zhou, Y., Tang, Z.: Collaborative filtering and deep learning based recommendation system for cold start items[J]. Expert Syst. Appl. 69(1), 29–39 (2017)

    Article  Google Scholar 

  6. Xu, C., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Zhuang, F., et al.: Graph Contextualized Self-Attention Network for Session-based Recommendation[C]. International Joint Conference on Artificial Intelligence, Macau, China, 3940–3946 (2019)

  7. Ouyang, Y., Guo, B., Tang, X., He, X., Xiong, J., Yu, Z.: Learning cross-domain representation with multi-graph neural network[J]. arXiv preprint arXiv:.10095 (2019)

  8. Zhang, M., Chen, Y.: Inductive matrix completion based on graph neural networks[C]. International Conference on Learning Representations, New Orleans, Louisiana, USA, 1–12 (2019)

  9. Zhang, J., Shi, X., Zhao, S., King, I.: Star-gcn: Stacked and reconstructed graph convolutional networks for recommender systems[C]. International Joint Conference on Artificial Intelligence, Macau, China, 4264 (2019)

  10. Berg, R.V.D., Kipf, T.N., Welling, M.: Graph convolutional matrix completion[J]. arXiv preprint arXiv:.02263 (2017)

  11. Sun, Z., Guo, Q., Yang, J., Fang, H., Guo, G., Zhang, J., et al.: Research commentary on recommendations with side information: A survey and research directions[J]. Electron. Commer. Res. Appl. 37(1), 100879 (2019)

    Article  Google Scholar 

  12. Ma, H., Zhou, D., Liu, C., Lyu, M.R., King, I.: Recommender systems with social regularization[C]. Proceedings of the 4th ACM International Conference on Web Search and Data Mining, HongKong, China, 287–296 (2011)

  13. Breese, J.S., Heckerman, D., Kadie, C.: Empirical analysis of predictive algorithms for collaborative filtering[J]. arXiv preprint arXiv:1301.7363 (2013)

  14. Wu, L., Sun, P., Hong, R., Fu, Y., Wang, X., Wang, M.: Socialgcn: An efficient graph convolutional network based model for social recommendation[J]. arXiv preprint arXiv:.02815 (2018)

  15. Jamali, M., Ester, M.: A matrix factorization technique with trust propagation for recommendation in social networks[C]. Proceedings of the 4th ACM Conference on Recommender Systems, Barcelona, Spain, 135–142 (2010)

  16. Singh, A.P., Gordon, G.J.: Relational learning via collective matrix factorization[C]. Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Las Vegas, Nevada, USA, 650–658 (2008)

  17. Wang, G., He, X., Ishuga, C.I.: Social and content aware One-Class recommendation of papers in scientific social networks[J]. PLoS ONE 12(8), e0181380 (2017)

    Article  Google Scholar 

  18. Wang, X., He, X., Wang, M., Feng, F., Chua, T.: Neural Graph Collaborative Filtering[C]. The 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 165–174 (2019)

  19. Covington, P., Adams, J., Sargin, E.: Deep neural networks for youtube recommendations[C]. Proceedings of the 10th ACM Conference on Recommender Systems, Boston, MA, USA, 191–198 (2016)

  20. Xiao, J., Ye, H., He, X., Zhang, H., Wu, F., Chua, T.-S.: Attentional factorization machines: Learning the weight of feature interactions via attention networks[C]. Proceedings of the 26th International Joint Conference on Artificial Intelligence, Melbourne, Australia, 3119–3125 (2017)

  21. Zhou, G., Mou, N., Fan, Y., Pi, Q., Bian, W., Zhou, C., et al.: Deep interest evolution network for click-through rate prediction[C]. Proceedings of the AAAI Conference on Artificial Intelligence, Honolulu, Hawaii, USA, 5941–5948 (2019)

  22. Zhou, G., Zhu, X., Song, C., Fan, Y., Zhu, H., Ma, X., et al.: Deep interest network for click-through rate prediction[C]. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, UK, 1059–1068 (2018)

  23. Zhou, J., Cui, G., Hu, S., Zhang, Z., Yang, C., Liu, Z., et al.: Graph neural networks: A review of methods and applications[J]. AI Open 1(1), 57–81 (2020)

    Article  Google Scholar 

  24. Hamilton, W.L., Ying, R., Leskovec, J.: Representation learning on graphs: Methods and applications[J]. arXiv preprint arXiv:.05584 (2017)

  25. Hu, L., Li, C., Shi, C., Yang, C., Shao, C.: Graph neural news recommendation with long-term and short-term interest modeling[J]. Inf. Process. Manag. 57(2), 102142 (2020)

    Article  Google Scholar 

  26. Wang, H., Lian, D., Ge, Y.: Binarized collaborative filtering with distilling graph convolutional networks[C]. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macau, China, 4802–4808 (2019)

  27. Zhou, P., Wu, Z., Wen, G., Tang, K., Ma, J.: Multi-scale graph classification with shared graph neural network[J]. World Wide Web, 1–18 (2022)

  28. Zitnik, M., Agrawal, M., Leskovec, J.: Modeling polypharmacy side effects with graph convolutional networks[J]. Bioinformatics 34(13), i457–i466 (2018)

    Article  Google Scholar 

  29. Qi, X., Liao, R., Jia, J., Fidler, S., Urtasun, R.: 3D graph neural networks for RGBD semantic segmentation[C]. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy, 5199–5208 (2017)

  30. Cui, Z., Henrickson, K., Ke, R., Wang, Y.: Traffic graph convolutional recurrent neural network: A deep learning framework for network-scale traffic learning and forecasting[J]. IEEE Trans. Intell. Transp. Syst. 21(11), 4883–4894 (2019)

    Article  Google Scholar 

  31. Gao, D., Li, K., Wang, R., Shan, S., Chen, X.: Multi-modal graph neural network for joint reasoning on vision and scene text[C]. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 12746–12756 (2020)

  32. Huang, P.-H., Lee, H.-H., Chen, H.-T., Liu, T.-L.: Text-guided graph neural networks for referring 3d instance segmentation[C]. Proceedings of the AAAI Conference on Artificial Intelligence, Vancouver, Canada, 1610–1618 (2021)

  33. Lai, Y., Zhang, L., Han, D., Zhou, R., Wang, G.: Fine-grained emotion classification of Chinese microblogs based on graph convolution networks[J]. World Wide Web 23(5), 2771–2787 (2020)

    Article  Google Scholar 

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

  35. Yin, R., Li, K., Zhang, G., Lu, J.: A deeper graph neural network for recommender systems[J]. Knowl.-Based Syst. 185, 105020 (2019)

    Article  Google Scholar 

  36. Wang, D., Wang, X., Xiang, Z., Yu, D., Deng, S., Xu, G.: Attentive sequential model based on graph neural network for next poi recommendation[J]. World Wide Web 24(6), 2161–2184 (2021)

    Article  Google Scholar 

  37. Li, Y., Chen, H., Sun, X., Sun, Z., Li, L., Cui, L., et al.: Hyperbolic hypergraphs for sequential recommendation[C]. Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Online, 988–997 (2021)

  38. Chen, H., Li, Y., Sun, X., Xu, G., Yin, H.: Temporal meta-path guided explainable recommendation[C]. Proceedings of the 14th ACM international conference on web search and data mining, Jerusalem, Israel, 1056–1064 (2021)

  39. Monti, F., Bronstein, M., Bresson, X.: Geometric matrix completion with recurrent multi-graph neural networks[C]. Advances in Neural Information Processing Systems, Long Beach, CA, USA, 30 (2017)

  40. Xu, Q., Shen, F., Liu, L., Shen, H.T.: Graphcar: Content-aware multimedia recommendation with graph autoencoder[C]. The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, Ann Arbor, Michigan, USA, 981–984 (2018)

  41. Schlichtkrull, M., Kipf, T.N., Bloem, P., Berg, R.V.D., Titov, I., Welling, M.: Modeling relational data with graph convolutional networks[C]. European Semantic Web Conference, Heraklion, Crete, 593–607 (2018)

  42. Liu, F., Cheng, Z., Zhu, L., Gao, Z., Nie, L.: Interest-aware message-passing gcn for recommendation[C]. Proceedings of the Web Conference, New York, US, 1296–1305 (2021)

  43. Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: Dropout: A simple way to prevent neural networks from overfitting[J]. J. Mach. Learn. Res. 15(1), 1929–1958 (2014)

    MathSciNet  MATH  Google Scholar 

  44. Wei, Y., Cheng, Z., Yu, X., Zhao, Z., Zhu, L., Nie, L.: Personalized hashtag recommendation for micro-videos[C]. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, 1446–1454 (2019)

  45. Wei, Y., Wang, X., Nie, L., He, X., Hong, R., Chua, T.-S.: MMGCN: Multi-modal graph convolution network for personalized recommendation of micro-video[C]. Proceedings of the 27th ACM International Conference on Multimedia, Nice, France, 1437–1445 (2019)

  46. Wang, H., Zhang, F., Zhang, M., Leskovec, J., Zhao, M., Li, W., et al.: Knowledge-aware graph neural networks with label smoothness regularization for recommender systems[C]. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Anchorage, Alaska, USA, 968–977 (2019)

  47. Ali, Z., Qi, G., Muhammad, K., Ali, B., Abro, W.A.: Paper recommendation based on heterogeneous network embedding[J]. Knowl.-Based Syst. 210, 106438 (2020)

    Article  Google Scholar 

  48. Belém, F.M., Martins, E.F., Almeida, J.M., Gonçalves, M.A.: Personalized and object-centered tag recommendation methods for web 2.0 applications[J]. Inf. Process. Manag. 50(4), 524–553 (2014)

    Article  Google Scholar 

  49. Yin, H., Wang, Q., Zheng, K., Li, Z., Yang, J., Zhou, X.: Social influence-based group representation learning for group recommendation[C]. IEEE 35th International Conference on Data Engineering (ICDE), Macau SAR, China, 566–577 (2019)

  50. Golub, G.H., Reinsch, C.: Singular value decomposition and least squares solutions [M]. Linear Algebra, 134–151. Springer, Berlin, Heidelberg (1971)

  51. Lee, D.D., Seung, H.S.: Learning the parts of objects by non-negative matrix factorization[J]. Nature 401(6755), 788–791 (1999)

    Article  MATH  Google Scholar 

  52. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.: Neural Collaborative Filtering[C]. Proceedings of the 26th International Conference on World Wide Web, Perth, Australia, 173–182 (2017)

  53. Hamilton, W., Ying, Z., Leskovec, J.: Inductive representation learning on large graphs[C]. Advances in Neural Information Processing Systems, Long Beach, CA, USA (2017)

  54. Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., Bengio, Y.: Graph attention networks[C]. International Conference on Learning Representations, Vancouver, BC, Canada, 1–12 (2018)

  55. Wang, X., Jin, H., Zhang, A., He, X., Xu, T., Chua, T.-S.: Disentangled graph collaborative filtering[C]. Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, Xi’an, China, 1001–1010 (2020)

  56. He, X., Deng, K., Wang, X., Li, Y., Zhang, Y., Wang, M.: Lightgcn: Simplifying and powering graph convolution network for recommendation[C]. Proceedings of the 43rd International ACM SIGIR conference on research and development in Information Retrieval, Xi’an, China, 639–648 (2020)

  57. Kipf, T., Welling, M.: Semi-Supervised Classification with Graph Convolutional Networks[J]. arXiv preprint arXiv:1609.02907 (2016)

  58. Wu, L., Sun, P., Fu, Y., Hong, R., Wang, X., Wang, M.: A neural influence diffusion model for social recommendation[C]. Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, Paris, France, 235–244 (2019)

Download references

Acknowledgements

We appreciate the National Natural Science Foundation of China, Science Fund for Distinguished Young Scholars of AnHui, Anhui Provincial Key Research and Development Program, and Fundamental Research Funds for the Central Universities for supporting this research.

Funding

This work is partially supported by the National Natural Science Foundation of China (72071062, 72071061), Science Fund for Distinguished Young Scholars of AnHui (2208085J12), Anhui Provincial Key Research and Development Program (202104a05020038), and Fundamental Research Funds for the Central Universities (PA2021KCPY0032).

Author information

Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, Anhui, 230009, People’s Republic of China

    Gang Wang, Hanru Wang & Ying Yang

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making (Hefei University of Technology), Ministry of Education, Hefei, Anhui, People’s Republic of China

    Gang Wang

  3. Ministry of Education Engineering Research Center for Intelligent Decision-Making & Information System Technologies, Hefei, 230009, China

    Gang Wang

  4. School of Management Science and Engineering, Tianjin University of Finance and Economics, Tianjin, 300222, People’s Republic of China

    Jing Liu

Authors
  1. Gang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hanru Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Ying Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed to the study's conception and design. Material preparation, data collection and analysis, and experiments were performed by Gang Wang and Hanru Wang. The first draft of the manuscript was written by Hanru Wang and revised by Gang Wang. All authors read and approved the final manuscript.

Corresponding author

Correspondence to Gang Wang.

Ethics declarations

Ethical approval and consent to participate

Not applicable.

Consent for publication

Not applicable.

Human and animal ethics

Not applicable.

Competing interests

The authors declare that they have no competing interests.

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 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

Wang, G., Wang, H., Liu, J. et al. Leveraging the fine-grained user preferences with graph neural networks for recommendation. World Wide Web 26, 1371–1393 (2023). https://doi.org/10.1007/s11280-022-01099-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-022-01099-y

Keywords

Search

Navigation