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GNNRec: gated graph neural network for session-based social recommendation model

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Abstract

Recommendation system utilizes user-item interactions and information on user’s attributes to infer the user’s interests and use them to make recommendations for the user. Graph neural network(GNN) has become more widely used in recommendation systems in recent years, because of their ability to naturally integrate node information and topology. However, most of the current recommendation methods based on graph structure only focus on a single recommendation domain (for example,session-based recommendation models or social recommendation models), without taking into account both the user’s behavior information and the user’s social relationships; Furthermore, session-based recommendation models usually use a recurrent neural network (RNN) to model user session, while RNN only focuses on the short-term impact of sessions and cannot cover all the information of sessions. Therefore, this paper proposes a novel session-based social recommendation model called GNNRec, which first utilizes gated graph neural network (GGNN) to represent users’ session information, and then uses graph attention network (GAT) to aggregate social information of users and friends on social networks to effectively model users’ interests. In this paper, experiments are conducted on two large datasets——Douban and Epinions, and the results show that the GNNRec model performs significantly better than current mainstream recommendation models.

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References

  • Atas, M., Felfernig, A., Polat-Erdeniz, S., et al. (2021). Towards psychology-aware preference construction in recommender systems: Overview and research issues. Journal of Intelligent Information System, 57, 467–489. https://doi.org/10.1007/s10844-021-00674-5

    Article  Google Scholar 

  • Chen, T., Wong, R. C. W. (2021). An efficient and effective framework for session-based social recommendation[C]. Proceedings of the 14th ACM International Conference on Web Search and Data Mining: 400–408. https://doi.org/10.1145/3437963.3441792

  • Fan, W., Ma, Y., Li, Q., et al. (2019). Graph neural networks for social recommendation[C]. The world wide web conference., 417–426. https://doi.org/10.1145/3308558.3313488

  • 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[C]. Proceedings of the AAAI Conference on Artificial Intelligence., 29(1), 123–129. https://doi.org/10.1609/aaai.v29i1.9153

    Article  Google Scholar 

  • Hidasi, B., Karatzoglou, A., Baltrunas, L., et al. (2016). Session-based recommendations with recurrent neural networks[J]. ICLR 4th International Conference on Learning Representations, 5, 1–10. https://doi.org/10.48550/arXiv.1511.06939

    Article  Google Scholar 

  • Jamali M, Ester M (2010) A matrix factorization technique with trust propagation for recommendation in social networks[C]. Proceedings of the fourth ACM conference on Recommender systems: 135–142. https://doi.org/10.1145/1864708.1864736.

  • Li, Y., Tarlow, D., Brockschmidt, M., et al. (2017a). Gated graph sequence neural networks[J]. ICLR 4th International Conference on Learning Representations, 9, 1–20. https://doi.org/10.48550/arXiv.1511.05493

    Article  Google Scholar 

  • Li, J., Ren, P., Chen, Z., et al. (2017b) Neural attentive session-based recommendation[C]. Proceedings of the 2017 ACM on Conference on Information and Knowledge Management: 1419–1428. https://doi.org/10.1145/3132847.3132926

  • Li, R., Lin, H., Shi, Y., et al. (2019) SocialST: Social liveness and trust enhancement based social recommendation[C]. 2019 IEEE International Conference on Web Services (ICWS). IEEE: 139–145. https://doi.org/10.1109/ICWS.2019.00033

  • Li, Y., Zhan, Z., Li, H., et al. (2022). Interest-aware influence diffusion model for social recommendation[J]. Journal of Intelligent Information Systems, 58(2), 363–377. https://doi.org/10.1007/s10844-021-00684-3

    Article  Google Scholar 

  • Liu, F., Cheng, Z., Zhu, L., et al. (2021). Interest-aware message-passing gcn for recommendation[C]. Proceedings of the Web Conference, 2021, 1296–1305. https://doi.org/10.1145/3442381.3449986

    Article  Google Scholar 

  • Lu, Y., Xie, R., Shi, C., et al. Social influence attentive neural network for friend-enhanced recommendation[C]. Joint European Conference on Machine Learning and Knowledge Discovery in Databases. Springer, Cham, 2020: 3–18. https://doi.org/10.1007/978-3-030-67667-4_1

  • Ma, H., Yang, H., Lyu, M. R. et al. (2008) Sorec: Social recommendation using probabilistic matrix factorization[C]. Proceedings of the 17th ACM conference on Information and Knowledge Management: 931–940. https://doi.org/10.1145/1458082.1458205.

  • Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L. (2009). BPR: Bayesian personalized ranking from implicit feedback. In Proceedingsof the twenty-fifth conference on uncertainty in artificial intelligence. 2009:452–461. https://doi.org/10.48550/arXiv.1205.2618

  • Scarselli, F., Gori, M., Tsoi, A. C., et al. (2008). The graph neural network model[J]. IEEE Transactions on Neural Networks, 20(1), 61–80. https://doi.org/10.1109/TNN.2008.2005605

    Article  Google Scholar 

  • Shokeen, J., & Rana, C. (2020). Social recommender systems: techniques, domains, metrics, datasets and future scope. Journal of Intelligent Information System, 54, 633–667. https://doi.org/10.1007/s10844-019-00578-5

    Article  Google Scholar 

  • Song, W., Xiao, Z., Wang, Y. et al. (2019) Session-based social recommendation via dynamic graph attention networks[C]. Proceedings of the Twelfth ACM International Conference on Web Search and Data Mining: 555–563. https://doi.org/10.1145/3289600.3290989

  • Song, L., Bi, Y., Yao, M. et al. (2020) DREAM: A Dynamic Relation-Aware Model for Social Recommendation[C]. Proceedings of the 29th ACM International Conference on Information & Knowledge Management: 2225–2228. https://doi.org/10.48550/arXiv.2008.04579

  • Veličković, P., Cucurull, G., Casanova, A., et al. (2018). Graph attention networks[J]. ICLR 4th International Conference on Learning Representations, 2, 1–12. https://doi.org/10.48550/arXiv.1710.10903

    Article  Google Scholar 

  • Wang, Z., Wei, W., Cong, G., et al. (2020) Global context enhanced graph neural networks for session-based recommendation[C]. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval: 169–178. https://doi.org/10.48550/arXiv.2106.05081

  • Wu, S., Tang, Y., Zhu, Y., et al. (2019a). Session-based recommendation with graph neural networks[C]. Proceedings of the AAAI Conference on Artificial Intelligence., 33(01), 346–353. https://doi.org/10.1609/aaai.v33i01.3301346

    Article  Google Scholar 

  • Wu, L., Sun, P., Fu, Y., et al. (2019b) A neural influence diffusion model for social recommendation[C]. Proceedings of the 42nd international ACM SIGIR Conference on Research and Development in Information Retrieval: 235–244. https://doi.org/10.1145/3331184.3331214

  • Wu, Z., Pan, S., Chen, F., et al. (2020a). A comprehensive survey on graph neural networks[J]. IEEE transactions on Neural Networks and Learning Systems, 32(1), 4–24. https://doi.org/10.48550/arXiv.1901.00596

    Article  MathSciNet  Google Scholar 

  • Wu, L., Li, J., Sun, P. et al. (2020b) Diffnet++: A neural influence and interest diffusion network for social recommendation[J]. IEEE Transactions on Knowledge and Data Engineering (1):1–1. https://doi.org/10.48550/arXiv.2002.00844

  • Yu, F., Zhu, Y., Liu, Q., et al. (2020) TAGNN: Target attentive graph neural networks for session-based recommendation[C]. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (5): 1921–1924. https://doi.org/10.1145/3397271.3401319

  • Zhang, M., Wu, S., Gao, M., et al. (2020). Personalized graph neural networks with attention mechanism for session-aware recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 31(9), 1–12. https://doi.org/10.48550/arXiv.1910.08887

    Article  Google Scholar 

  • Zhao, T., McAuley, J., King, I. (2014) Leveraging social connectionsto improve personalized ranking for collaborative filtering [C]. In Proceedings of the 23rd ACM International Conference on Information and Knowledge Management: 261–270. https://doi.org/10.1145/2661829.2661998.

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  1. School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, China

    Chun Liu, Yuxiang Li, Hong Lin & Chaojie Zhang

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  1. Chun Liu
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  2. Yuxiang Li
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  4. Chaojie Zhang
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Correspondence to Hong Lin.

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Liu, C., Li, Y., Lin, H. et al. GNNRec: gated graph neural network for session-based social recommendation model. J Intell Inf Syst 60, 137–156 (2023). https://doi.org/10.1007/s10844-022-00733-5

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