Nothing Special   »   [go: up one dir, main page]
Skip to main content
Springer Nature Link
Log in

Community aware graph embedding learning for item recommendation

  • Published:
World Wide Web Aims and scope Submit manuscript

Abstract

Due to the heterogeneity of a large amount of real-world data, meta-paths are widely used in recommendation. Such recommendation methods can represent composite relationships between entities, but cannot explore reliable relations between nodes and influence among meta-paths. For solving this problem, a Community Aware Graph Embedding Learning method for Item Recommendation(CAEIRec) is proposed. By adaptively constructing communities for nodes in the graph of entities, the correlations of nodes are embedded in graph learning from the aspect of community structure. Semantic information of users and items are jointly learnt in the embedding. Finally, the embeddings of users and items are fed to extend matrix factorization for getting the top recommendations. A series of comprehensive experiments are conducted on two different public datasets. The empirical results show that CAEIRec is an encouraging recommendation method by the comarison with the state-of-the-art methods. Source code of CAEIRec is available at https://github.com/a545187002/CAEIRec-tensorflow.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Algorithm 1
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

Availability of data and materials

The datasets are public datasets.

Notes

  1. https://www.heywhale.com/mw/dataset/60e3b1d8be5f090017611215/file

References

  1. Linden, G., Smith, B., York, J.: Amazon. com recommendations: Item-to-item collaborative filtering. IEEE Internet Comput 7(1), 76–80 (2003)

    Article  Google Scholar 

  2. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: Proceedings of the 26th international conference on world wide Web, pp. 173–182 (2017)

  3. Koren, Y.: Factorization meets the neighborhood: a multifaceted collaborative filtering model. In: Proceedings of the 14th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 426–434 (2008)

  4. Sedhain, S., Menon, A.K., Sanner, S., Xie, L.: Autorec: Autoencoders meet collaborative filtering. In: Proceedings of the 24th international conference on world wide Web, pp. 111–112 (2015)

  5. Fan, W., Ma, Y., Li, Q., He, Y., Zhao, E., Tang, J., Yin, D.: Graph neural networks for social recommendation. In: The world wide Web conference, pp. 417–426 (2019)

  6. 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, pp. 639–648 (2020)

  7. Shi, C., Hu, B., Zhao, W.X., Philip, S.Y.: Heterogeneous information network embedding for recommendation. IEEE Trans. Knowl. Data Eng. 31(2), 357–370 (2018)

    Article  Google Scholar 

  8. 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, pp. 950–958 (2019)

  9. Gao, C., Wang, X., He, X., Li, Y.: Graph neural networks for recommender system. In: Proceedings of the fifteenth ACM international conference on Web search and data mining, pp. 1623–1625 (2022)

  10. Wu, S., Sun, F., Zhang, W., Xie, X., Cui, B.: Graph neural networks in recommender systems: a survey. ACM Comput. Surv. 55(5), 1–37 (2022)

    Article  Google Scholar 

  11. Lu, Y., Fang, Y., Shi, C.: Meta-learning on heterogeneous information networks for cold-start recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp. 1563–1573 (2020)

  12. Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2016)

    Article  Google Scholar 

  13. Liu, F., Xue, S., Wu, J., Zhou, C., Hu, W., Paris, C., Nepal, S., Yang, J., Yu, P.S.: Deep learning for community detection: Progress, challenges and opportunities. In: Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence, pp. 4981–4987 (2020)

  14. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)

    Article  Google Scholar 

  15. Kabbur, S., Ning, X., Karypis, G.: Fism: factored item similarity models for top-n recommender systems. In: Proceedings of the 19th ACM SIGKDD international conference on knowledge discovery and data mining, pp. 659–667 (2013)

  16. 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, pp. 165–174 (2019)

  17. Gong, J., Wang, S., Wang, J., Feng, W., Peng, H., Tang, J., Yu, P.S.: Attentional graph convolutional networks for knowledge concept recommendation in moocs in a heterogeneous view. In: Proceedings of the 43rd international ACM SIGIR conference on research and development in information retrieval, pp. 79–88 (2020)

  18. Hao, P., Li, Y., Bai, C.: Meta-relationship for course recommendation in moocs. Multimed. Syst. 29(1), 235–246 (2023)

    Article  Google Scholar 

  19. Chen, L., Cao, J., Wang, Y., Liang, W., Zhu, G.: Multi-view graph attention network for travel recommendation. Expert Syst. Appl. 191, 116234 (2022)

    Article  Google Scholar 

  20. Zhang, H., Shen, X., Yi, B., Wang, W., Feng, Y.: Kgan: Knowledge grouping aggregation network for course recommendation in moocs. Expert Syst. Appl. 211, 118344 (2023)

    Article  Google Scholar 

  21. Zhang, X., Xu, S., Lin, W., Wang, S.: Constrained social community recommendation. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp. 5586–5596 (2023)

  22. Wei, Y., Ma, H., Zhang, R., Li, Z., Chang, L.: Exploring implicit relationships in social network for recommendation systems. In: Pacific-Asia conference on knowledge discovery and data mining, pp. 386–397 (2021)

  23. Rostami, M., Muhammad, U., Forouzandeh, S., Berahmand, K., Farrahi, V., Oussalah, M.: An effective explainable food recommendation using deep image clustering and community detection. Intell. Syst. Appl. 16, 200157 (2022)

    Google Scholar 

  24. Ye, B., Mao, S., Hao, P., Chen, W., Bai, C.: Community enhanced course concept recommendation in moocs with multiple entities. In: Knowledge science, engineering and management: 14th international conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part II 14, pp. 279–293 (2021)

  25. Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space. arXiv:1301.3781 (2013)

  26. Tong, H., Faloutsos, C., Pan, J.-Y.: Fast random walk with restart and its applications. In: Sixth international conference on data mining (ICDM’06), pp. 613–622 (2006)

  27. Yu, J., Luo, G., Xiao, T., Zhong, Q., Wang, Y., Feng, W., Luo, J., Wang, C., Hou, L., Li, J., et al.: Mooccube: a large-scale data repository for nlp applications in moocs. In: Proceedings of the 58th annual meeting of the association for computational linguistics, pp. 3135–3142 (2020)

  28. Wang, X., Ji, H., Shi, C., Wang, B., Ye, Y., Cui, P., Yu, P.S.: Heterogeneous graph attention network. In: The world wide Web conference, pp. 2022–2032 (2019)

  29. Yang, Y., Guan, Z., Li, J., Zhao, W., Cui, J., Wang, Q.: Interpretable and efficient heterogeneous graph convolutional network. IEEE Trans. Knowl. Data Eng. (2021)

  30. Zheng, S.S., Guan, D., Yuan, W.: Semantic-aware heterogeneous information network embedding with incompatible meta-paths. World Wide Web, 1–21 (2022)

Download references

Funding

This work is supported by Natural Science Foundations of China under Grant No. U20A20196 and No. 61976192.

Author information

Author notes

  1. Pengyi Hao and Zhaojie Qian contributed equally to this work.

Authors and Affiliations

  1. College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, China

    Pengyi Hao, Zhaojie Qian, Shuang Wang & Cong Bai

Authors
  1. Pengyi Hao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Zhaojie Qian
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Shuang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Cong Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Qian conducted the creation of model, performed the data curation and wrote the manuscript. Hao performed the analysis of the data and reviewed and revised the manuscript. Wang and Bai reviewed the manuscript.

Corresponding author

Correspondence to Cong Bai.

Ethics declarations

Ethical approval

Not applicable.

Competing interests

The authors declare 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 (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

Hao, P., Qian, Z., Wang, S. et al. Community aware graph embedding learning for item recommendation. World Wide Web 26, 4093–4108 (2023). https://doi.org/10.1007/s11280-023-01224-5

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11280-023-01224-5

Keywords

Search

Navigation