Skip to main content
Book cover

European Semantic Web Conference

ESWC 2022: The Semantic Web pp 3–20Cite as

Enhancing Sequential Recommendation via Decoupled Knowledge Graphs

  • 490 Accesses

Part of the Lecture Notes in Computer Science book series (LNCS,volume 13261)

Abstract

Sequential recommendation can capture dynamic interest patterns of users based on user interaction sequences. Recently, there has been interest in integrating the knowledge graph (KG) into sequential recommendation. Existing works suffer from two main challenges: a) representing each entity in the KG as a single vector can confound heterogeneous information about the entity; b) triple-based facts are modeled independently, lacking the exploration of high-order connectivity between entities. To solve the above challenges, we decouple the KG into two subgraphs, namely CRoss-user Behavior-based graph and Intrinsic Attribute-based graph (Crbia), depending on the type of relation between entities. We further propose a CrbiaNet based on the two subgraphs. First, CrbiaNet obtains behavior-level and attribute-level semantic features from these two subgraphs independently by different graph neural networks, respectively. Then, CrbiaNet applies a sequential model incorporating these semantic features to capture dynamic preference of the users. Extensive experiments on three real-world datasets show that our proposed CrbiaNet outperforms previous state-of-the-art knowledge-enhanced sequential recommendation models by a large margin consistently.

Keywords

  • Sequential recommendation
  • Knowledge graph
  • Heterogeneous information
  • Graph neural network

This is a preview of subscription content, access via your institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-031-06981-9_1
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-031-06981-9
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Learn about institutional subscriptions
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.

Notes

  1. 1.

    The codes are released at https://github.com/paulpig/sequentialRec.git..

References

  1. Bordes, A., Usunier, N., Garcia-Duran, A., Weston, J., Yakhnenko, O.: Translating embeddings for modeling multi-relational data. In: Advances in Neural Information Processing Systems, vol. 26 (2013)

    Google Scholar 

  2. Cho, K., et al.: Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078 (2014)

  3. Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018)

  4. Guo, W., et al.: Dual graph enhanced embedding neural network for ctrprediction. arXiv preprint arXiv:2106.00314 (2021)

  5. He, R., McAuley, J.: Ups and downs: modeling the visual evolution of fashion trends with one-class collaborative filtering. In: Proceedings of the 25th International Conference on World Wide Web, pp. 507–517 (2016)

    Google Scholar 

  6. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. arXiv preprint arXiv:1511.06939 (2015)

  7. Huang, J., Zhao, W.X., Dou, H., Wen, J.-R., Chang, E.Y.: Improving sequential recommendation with knowledge-enhanced memory networks. In: The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval, pp. 505–514 (2018)

    Google Scholar 

  8. Huang, X., Qian, S., Fang, Q., Sang, J., Xu, C.: Meta-path augmented sequential recommendation with contextual co-attention network. ACM Trans. Multimed. Comput. Commun. Appl. (TOMM) 16(2), 1–24 (2020)

    CrossRef  Google Scholar 

  9. Kang, W.-C., McAuley, J.: Self-attentive sequential recommendation. In: 2018 IEEE International Conference on Data Mining (ICDM), pp. 197–206. IEEE (2018)

    Google Scholar 

  10. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

  11. Kosinski, M., Stillwell, D., Graepel, T.: Private traits and attributes are predictable from digital records of human behavior. Proc. Natl. Acad. Sci. 110(15), 5802–5805 (2013)

    CrossRef  Google Scholar 

  12. Li, J., Ren, P., Chen, Z., Ren, Z., Lian, T., Ma, J.: Neural attentive session-based recommendation. In: Proceedings of the 2017 ACM on Conference on Information and Knowledge Management, pp. 1419–1428 (2017)

    Google Scholar 

  13. Lin, Y., Liu, Z., Sun, M., Liu, Y., Zhu, X.: Learning entity and relation embeddings for knowledge graph completion. In: Twenty-Ninth AAAI Conference on Artificial Intelligence (2015)

    Google Scholar 

  14. Long, X., et al.: Social recommendation with self-supervised metagraph informax network. arXiv preprint arXiv:2110.03958 (2021)

  15. Ma, W., et al.: Jointly learning explainable rules for recommendation with knowledge graph. In: The World Wide Web Conference, pp. 1210–1221 (2019)

    Google Scholar 

  16. Oono, K., Suzuki, T.: Graph neural networks exponentially lose expressive power for node classification. arXiv preprint arXiv:1905.10947 (2019)

  17. Rendle, S.: Factorization machines. In: IEEE International Conference on Data Mining, pp. 995–1000. IEEE (2010)

    Google Scholar 

  18. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. arXiv preprint arXiv:1205.2618 (2012)

  19. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: Proceedings of the 19th International Conference on World Wide Web, pp. 811–820 (2010)

    Google Scholar 

  20. Shani, G., Heckerman, D., Brafman, R.I., Boutilier, C.: An MDP-based recommender system. J. Mach. Learn. Res. 6(9) (2005)

    Google Scholar 

  21. Sun, F., et al.: BERT4Rec: sequential recommendation with bidirectional encoder representations from transformer. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, pp. 1441–1450 (2019)

    Google Scholar 

  22. Tang, J., Wang, K.: Personalized top-n sequential recommendation via convolutional sequence embedding. In: Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining, pp. 565–573 (2018)

    Google Scholar 

  23. Taylor, W.L.: “Cloze procedure’’: a new tool for measuring readability. J. Q. 30(4), 415–433 (1953)

    Google Scholar 

  24. Trouillon, T., Welbl, J., Riedel, S., Gaussier, É., Bouchard, G.: Complex embeddings for simple link prediction. In: International Conference on Machine Learning, pp. 2071–2080. PMLR (2016)

    Google Scholar 

  25. Vaswani, A., et al.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

    Google Scholar 

  26. Wang, C., Ma, W., Zhang, M., Chen, C., Liu, Y., Ma, S.: Toward dynamic user intention: temporal evolutionary effects of item relations in sequential recommendation. ACM Trans. Inf. Syst. (TOIS) 39(2), 1–33 (2020)

    Google Scholar 

  27. Wang, C., Zhang, M., Ma, W., Liu, Y., Ma, S.: Make it a chorus: knowledge-and time-aware item modeling for sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 109–118 (2020)

    Google Scholar 

  28. Wang, M., Qiu, L., Wang, X.: A survey on knowledge graph embeddings for link prediction. Symmetry 13(3), 485 (2021)

    CrossRef  Google Scholar 

  29. Wang, P., Fan, Y., Xia, L., Zhao, W.X., Niu, S., Huang, J.: Kerl: a knowledge-guided reinforcement learning model for sequential recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 209–218 (2020)

    Google Scholar 

  30. 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)

    Google Scholar 

  31. Wu, L., Yang, Y., Zhang, K., Hong, R., Fu, Y., Wang, M.: Joint item recommendation and attribute inference: an adaptive graph convolutional network approach. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 679–688 (2020)

    Google Scholar 

  32. Xia, L., et al.: Knowledge-enhanced hierarchical graph transformer network for multi-behavior recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 5, pp. 4486–4493 (2021)

    Google Scholar 

  33. Xie, T., Xu, Y., Chen, L., Liu, Y., Zheng, Z.: Sequential recommendation on dynamic heterogeneous information network. In: 2021 IEEE 37th International Conference on Data Engineering (ICDE), pp. 2105–2110. IEEE (2021)

    Google Scholar 

  34. Xin, X., He, X., Zhang, Y., Zhang, Y., Jose, J.: Relational collaborative filtering: modeling multiple item relations for recommendation. In: Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 125–134 (2019)

    Google Scholar 

  35. Yang, B., Yih, W.-T., He, X., Gao, J., Deng, L.: Embedding entities and relations for learning and inference in knowledge bases. arXiv preprint arXiv:1412.6575 (2014)

  36. Yang, Z., Dong, S., Hu, J.: GFE: general knowledge enhanced framework for explainable sequential recommendation. Knowl.-Based Syst. 230, 107375 (2021)

    Google Scholar 

  37. Yuan, F., Karatzoglou, A., Arapakis, I., Jose, J.M., He, X.: A simple convolutional generative network for next item recommendation. In: Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining, pp. 582–590 (2019)

    Google Scholar 

  38. Zhang, T., et al.: Feature-level deeper self-attention network for sequential recommendation. In: IJCAI, pp. 4320–4326 (2019)

    Google Scholar 

  39. Zhao, X., Cheng, Z., Zhu, L., Zheng, J., Li, X.: UGRec: modeling directed and undirected relations for recommendation. arXiv preprint arXiv:2105.04183 (2021)

  40. Zhou, K., et al.: S3-Rec: self-supervised learning for sequential recommendation with mutual information maximization. In: Proceedings of the 29th ACM International Conference on Information & Knowledge Management, pp. 1893–1902 (2020)

    Google Scholar 

  41. Zhu, Q., Zhou, X., Wu, J., Tan, J., Guo, L.: A knowledge-aware attentional reasoning network for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 04, pp. 6999–7006 (2020)

    Google Scholar 

Download references

Acknowledgement

This research project was supported by the Foundation of Science and Technology Project of Hebei Education Department (Grants No. ZD2021063).

Author information

Authors and Affiliations

  1. Collaborative Innovation Center, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Bingchao Wu, Chenglong Deng, Bei Guan & Yongji Wang

  2. State Key Laboratory of Computer Science, Institute of Software, Chinese Academy of Sciences, Beijing, 100190, China

    Yongji Wang & Yuxuan Kangyang

  3. University of Chinese Academy of Sciences, Beijing, 100049, China

    Bingchao Wu, Chenglong Deng, Bei Guan, Yongji Wang & Yuxuan Kangyang

Authors
  1. Bingchao Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Chenglong Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bei Guan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Yongji Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yuxuan Kangyang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yongji Wang .

Editor information

Editors and Affiliations

  1. University of Amsterdam, Amsterdam, Noord-Holland, The Netherlands

    Paul Groth

  2. Universidad Simón Bolívar, Leibniz Information Centre for Science and Technology, Hannover, Niedersachsen, Germany

    Maria-Esther Vidal

  3. Institut Polytechnique de Paris "DIG", Télécom ParisTech, Palaiseau, France

    Fabian Suchanek

  4. University of Southern California, Marina del Rey, CA, USA

    Pedro Szekley

  5. IBM Research - Thomas J. Watson Research, Yorktown Heights, NY, USA

    Pavan Kapanipathi

  6. LaSIGE, Fac de Ciencias,Edif C6, Pis0 3, Universidade de Lisboa, Lisbon, Portugal

    Dr. Catia Pesquita

  7. University of Nantes, Nantes, France

    Hala Skaf-Molli

  8. Aalto University, Espoo, Finland

    Minna Tamper

Rights and permissions

Reprints and Permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Verify currency and authenticity via CrossMark

Cite this paper

Wu, B., Deng, C., Guan, B., Wang, Y., Kangyang, Y. (2022). Enhancing Sequential Recommendation via Decoupled Knowledge Graphs. In: , et al. The Semantic Web. ESWC 2022. Lecture Notes in Computer Science, vol 13261. Springer, Cham. https://doi.org/10.1007/978-3-031-06981-9_1

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-06981-9_1

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-06980-2

  • Online ISBN: 978-3-031-06981-9

  • eBook Packages: Computer ScienceComputer Science (R0)