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Category-aware self-supervised graph neural network for session-based recommendation

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Abstract

Session-based recommendation which focuses on predicting the next behavior according to anonymous sessions of behavior records plays an important role in real-world applications. Most previous session-based recommendation approaches capture the preferences of users by modeling the behavior records between users and items within current session. However, items’ category information is not fully exploited, while existing works are still suffering from the severe issue of data sparsity. In this work, we propose a novel session-based recommendation model, namely Category-aware Self-supervised Graph Neural Network (namely CSGNN), which adopts a pre-training layer for capturing the features of items and categories, as well as the correlations among them. Especially, we build a category-aware heterogeneous hypergraph composed of item nodes and category nodes, which enhances the information learning in the current session. Then we design item-level and category-level self-attention models to represent the information of item and category, respectively, and integrate global and local preference of user for session-based recommendation. Finally, we combine self-supervised learning by constructing a category-aware session graph to further enhance the performance CSGNN and alleviate the data sparsity problem. Comprehensive experiments are conducted on three real-world datasets, Nowplaying, Diginetica, and Tmall, and the results show that the proposed model CSGNN achieves better performance than session-based recommendation baselines with several state-of-the-art approaches.

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Data Availability

No datasets were generated or analysed during the current study.

Notes

  1. https://www.kaggle.com/chelseapower/nowplayingrs

  2. https://competitions.codalab.org/competitions/11161

  3. https://tianchi.aliyun.com/dataset/dataDetail?dataId=42

  4. https://github.com/HduDBSI/CSGNN

References

  1. Wang, S., Cao, L., Wang, Y., Sheng, Q.Z., Orgun, M.A., Lian, D.: A survey on session-based recommender systems. ACM Comput. Surv. (CSUR) 54(7), 1–38 (2021)

    Article  Google Scholar 

  2. Deng, S., Wang, D., Li, X., Xu, G.: Exploring user emotion in microblogs for music recommendation. Expert Syst. Appl. 42(23), 9284–9293 (2015)

    Article  Google Scholar 

  3. Cui, Z., Chen, H., Cui, L., Liu, S., Liu, X., Xu, G., Yin, H.: Reinforced kgs reasoning for explainable sequential recommendation. World Wide Web 25(2), 631–654 (2022)

    Article  Google Scholar 

  4. Xu, C., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Zhuang, F., Fang, J., Zhou, X.: Graph contextualized self-attention network for session-based recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, vol. 19, pp. 3940–3946 (2019)

  5. Sarwar, B., Karypis, G., Konstan, J., Riedl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th International Conference on World Wide Web, pp. 285–295 (2001)

  6. Garcin, F., Dimitrakakis, C., Faltings, B.: Personalized news recommendation with context trees. In: Proceedings of the 7th ACM Conference on Recommender Systems, pp. 105–112 (2013)

  7. Hariri, N., Mobasher, B., Burke, R.: Context-aware music recommendation based on latenttopic sequential patterns. In: Proceedings of the Sixth ACM Conference on Recommender Systems, pp. 131–138 (2012)

  8. Hidasi, B., Karatzoglou, A., Baltrunas, L., Tikk, D.: Session-based recommendations with recurrent neural networks. Proceedings of the International Conference on Learning Representations (2015)

  9. Tan, Y.K., Xu, X., Liu, Y.: Improved recurrent neural networks for session-based recommendations. In: Proceedings of the 1st Workshop on Deep Learning for Recommender Systems, pp. 17–22 (2016)

  10. 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)

  11. Liu, Q., Zeng, Y., Mokhosi, R., Zhang, H.: Stamp: short-term attention/memory priority model for session-based recommendation. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 1831–1839 (2018)

  12. Wu, S., Tang, Y., Zhu, Y., Wang, L., Xie, X., Tan, T.: Session-based recommendation with graph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 346–353 (2019)

  13. Wu, X., He, H., Yang, H., Tai, Y., Wang, Z., Zhang, W.: Pda-gnn: propagation-depth-aware graph neural networks for recommendation. World Wide Web 26(5), 3585–3606 (2023)

    Article  Google Scholar 

  14. Cai, Z., Yuan, G., Zhuang, X., Wang, S., Qiao, S., Zhu, M.: Adaptive self-propagation graph convolutional network for recommendation. World Wide Web 1–24 (2023)

  15. Xia, X., Yin, H., Yu, J., Wang, Q., Cui, L., Zhang, X.: Self-supervised hypergraph convolutional networks for session-based recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, pp. 4503–4511 (2021)

  16. Cai, R., Wu, J., San, A., Wang, C., Wang, H.: Category-aware collaborative sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 388–397 (2021)

  17. Yuan, X., Duan, D., Tong, L., Shi, L., Zhang, C.: Icai-sr: item categorical attribute integrated sequential recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 1687–1691 (2021)

  18. Salamat, A., Luo, X., Jafari, A.: Heterographrec: a heterogeneous graph-based neural networks for social recommendations. Knowl.-Based Syst. 217, 106817 (2021)

  19. Tang, J., Qu, M., Wang, M., Zhang, M., Yan, J., Mei, Q.: Line: large-scale information network embedding. In: Proceedings of the 24th International Conference on World Wide Web, pp. 1067–1077 (2015)

  20. Zhou, K., Wang, H., Zhao, W.X., Zhu, Y., Wang, S., Zhang, F., Wang, Z., Wen, J.-R.: 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)

  21. Wang, D., Zhang, X., Wan, Y., Yu, D., Xu, G., Deng, S.: Modeling sequential listening behaviors with attentive temporal point process for next and next new music recommendation. IEEE Trans. Multimed. 1–13 (2021)

  22. Yang, Y., Zhou, S., Weng, H., Wang, D., Zhang, X., Yu, D., Deng, S.: Siamese learning based on graph differential equation for next-poi recommendation. Appl. Soft Comput. 150, 111086 (2024)

    Article  Google Scholar 

  23. 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)

  24. Shani, G., Heckerman, D., Brafman, R.I.: An mdp-based recommender system. J. Mach. Learn. Res. 6, 1265–1295 (2005)

    MathSciNet  Google Scholar 

  25. Zhang, T., Zhao, P., Liu, Y., Sheng, V.S., Xu, J., Wang, D., Liu, G., Zhou, X.: Feature-level deeper self-attention network for sequential recommendation. In: Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, IJCAI-19, pp. 4320–4326 (2019)

  26. Wang, S., Hu, L., Wang, Y., He, X., Sheng, Q.Z., Orgun, M.A., Cao, L., Ricci, F., Yu, P.S.: Graph learning based recommender systems: a review. In: Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI-21, pp. 4644–4652 (2021)

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

    Article  Google Scholar 

  28. Wang, X., Wang, D., Yu, D., Wu, R., Yang, Q., Deng, S., Xu, G.: Intent-aware graph neural network for point-of-interest embedding and recommendation. Neurocomputing 557, 126734 (2023)

    Article  Google Scholar 

  29. Wang, D., Zhang, X., Yin, Y., Yu, D., Xu, G., Deng, S.: Multi-view enhanced graph attention network for session-based music recommendation. ACM Trans. Inf. Syst. 42(1), 1–30 (2023)

    Google Scholar 

  30. Berg, R.v.d., Kipf, T.N., Welling, M.: Graph convolutional matrix completion. arXiv:1706.02263 (2017)

  31. 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)

  32. 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)

  33. Wu, J., Wang, X., Feng, F., He, X., Chen, L., Lian, J., Xie, X.: Self-supervised graph learning for recommendation. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 726–735 (2021)

  34. Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, Ł., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems, pp. 5998–6008 (2017)

  35. Wang, D., Wan, F., Yu, D., Shen, Y., Xiang, Z., Xu, Y.: Context-and category-aware double self-attention model for next poi recommendation. Appl. Intell. 53(15), 18355–18380 (2023)

    Article  Google Scholar 

  36. Zhou, C., Bai, J., Song, J., Liu, X., Zhao, Z., Chen, X., Gao, J.: Atrank: an attention-based user behavior modeling framework for recommendation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 32, pp. 4564–4571 (2018)

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

  38. Huang, X., Qian, S., Fang, Q., Sang, J., Xu, C.: Csan: contextual self-attention network for user sequential recommendation. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 447–455 (2018)

  39. Wang, Z., Wei, W., Cong, G., Li, X.-L., Mao, X.-L., Qiu, M.: Global context enhanced graph neural networks for session-based recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 169–178 (2020)

  40. Zhang, J., Wang, D., Yu, D.: Tlsan: time-aware long-and short-term attention network for next-item recommendation. Neurocomputing 441, 179–191 (2021)

  41. Yin, F., Ji, M., Li, S., Wang, Y.: Neural tv program recommendation with heterogeneous attention. Knowl. Inf. Syst. 1–21 (2022)

  42. Benson, A.R., Gleich, D.F., Leskovec, J.: Higher-order organization of complex networks. Science 353(6295), 163–166 (2016)

    Article  Google Scholar 

  43. Ji, S., Feng, Y., Ji, R., Zhao, X., Tang, W., Gao, Y.: Dual channel hypergraph collaborative filtering. In: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, pp. 2020–2029 (2020)

  44. Feng, Y., You, H., Zhang, Z., Ji, R., Gao, Y.: Hypergraph neural networks. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 3558–3565 (2019)

  45. 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, pp. 452–461 (2009)

  46. Xia, X., Yin, H., Yu, J., Shao, Y., Cui, L.: Self-supervised graph co-training for session-based recommendation. In: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, pp. 2180–2190 (2021)

  47. Lin, Z., Tian, C., Hou, Y., Zhao, W.X.: Improving graph collaborative filtering with neighborhood-enriched contrastive learning. In: Proceedings of the ACM Web Conference 2022, pp. 2320–2329 (2022)

  48. Wang, W., Xu, Y., Feng, F., Lin, X., He, X., Chua, T.-S.: Diffusion recommender model. In: Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 832–841 (2023)

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Funding

This research was supported by the National Natural Science Foundation of China under Grant No.62202131, 62372145, U20A20173, and 62125206, and the Key Research Project of Zhejiang Province under Grant 2022C01145.

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Authors and Affiliations

  1. School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, China

    Dongjing Wang, Ruijie Du, Qimeng Yang, Dongjin Yu, Feng Wan & Xiaojun Gong

  2. School of Computer Science and Data Science Institute, University of Technology Sydney, Sydney, Australia

    Guandong Xu

  3. School of Computer Science and Technology, Zhejiang University, Hangzhou, China

    Shuiguang Deng

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  1. Dongjing Wang
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Contributions

DJ.W. and RJ.D. conceived and designed the study, performed data analysis, and wrote the manuscript. DJ.W., QM.Y. and F.W. conducted experiments, collected data, and contributed to data interpretation. DJ.Y., XJ.G. and GD.X. contributed to the study design and provided critical revisions. QM.Y. and F.W. contributed to the literature review and assisted with manuscript preparation. DJ.Y. and SG.D supervised the project and provided overall guidance throughout the research process. All authors reviewed the manuscript and approved the final version.

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Correspondence to Dongjin Yu.

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Wang, D., Du, R., Yang, Q. et al. Category-aware self-supervised graph neural network for session-based recommendation. World Wide Web 27, 61 (2024). https://doi.org/10.1007/s11280-024-01299-8

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