结合图同构和混合阶残差门控图神经网络的会话推荐

doi:10.3778/j.issn.1673-9418.2401029

摘要/Abstract

摘要： 基于会话推荐的目的是依据当前会话的先前动作来预测用户的下一个动作。针对现有基于图神经网络的会话推荐模型存在的不足之处，提出一种结合图同构和混合阶残差门控图神经网络的会话推荐模型（GIHR-GNN）。使用图同构网络聚合相邻项目的特征向量，有效融合全局和局部信息，解决图神经网络善于捕获节点之间的局部连接而忽略全局信息的问题，并通过门控融合函数聚合用户的长短期兴趣以更好地捕捉用户兴趣的动态变化。使用混合阶门控图神经网络对位置嵌入向量进行处理以捕获用户长时间后重新交互所反映出的用户意图，并在此基础之上添加残差模块，解决深层网络的退化问题。将未去噪和去噪后的用户长期兴趣表示进行对比学习，缓解了数据稀疏和噪声干扰的问题。在Tmall和RetailRocket两个数据集上进行多次实验，并与先进基线模型进行比较，结果表明该模型在Tmall数据集上P@20指标和MRR@20指标至少提升了3.26%和10.33%，在RetailRocket数据集上P@20指标和MRR@20指标至少提升了0.55%和2.57%，证明了GIHR-GNN模型的有效性。

Abstract: Session-based recommendation aims to predict which item will be clicked next for the current session. Aiming at the shortcomings of existing session recommendation models based on graph neural network, this paper proposes a model named graph isomorphism and hybrid-order residual gated graph neural network for session-based recommendation (GIHR-GNN). Firstly, graph isomorphic network is used to aggregate feature vectors of adjacent items, which can effectively fuse global and local information, solving the problem that graph neural network is good at capturing local connections between nodes and ignoring global information. And the user􀆳s long-term and short-term interests are aggregated by gated fusion to capture dynamic changes of user interests. Secondly, hybrid-order gated graph neural network is used to process the position embedding to capture the user intention reflected by the user􀆳s re-interaction after a long time, and the residual module is added to solve the degradation problem of deep network. Finally, contrastive learning is applied to comparing the user􀆳s long-term interest representations without denoising and after denoising, alleviating the problems of data sparsity and noise interference. Experiments are performed on Tmall and RetailRocket datasets. Compared with baseline model, GIHR-GNN increases at least 3.26% and 10.33% in P@20 and MRR@20 on Tmall, 0.55% and 2.57% in P@20 and MRR@20 on RetailRocket, which proves the effectiveness of GIHR-GNN.

Key words: session recommendation, graph isomorphic network, hybrid-order residual gated graph neural network, contrastive learning

王永贵, 于琦. 结合图同构和混合阶残差门控图神经网络的会话推荐[J]. 计算机科学与探索, 2025, 19(2): 502-512.

WANG Yonggui, YU Qi. Graph Isomorphism and Hybrid-Order Residual Gated Graph Neural Network for Session-Based Recommendation[J]. Journal of Frontiers of Computer Science and Technology, 2025, 19(2): 502-512.

参考文献

[1] ZHAO Z W, WANG X Y, XIAO Y Y. Combining multi-head attention and sparse multi-head attention networks for session-based recommendation[C]//Proceedings of the 2023 International Joint Conference on Neural Networks. Piscataway: IEEE, 2023: 1-8.
[2] WANG S J, CAO L B, WANG Y, et al. A survey on session-based recommender systems[J]. ACM Computing Surveys, 2021, 54(7): 1-38.
[3] DONG L Y, ZHU G T, WANG Y Q, et al. A graph positional attention network for session-based recommendation[J]. IEEE Access, 2023, 11: 7564-7573.
[4] GAO C, WANG X, HE X, et al. Graph neural networks for recommender system[C]//Proceedings of the 15th ACM International Conference on Web Search and Data Mining. New York: ACM, 2022: 1623-1625.
[5] CHEN D L, LIN Y K, LI W, et al. Measuring and relieving the over-smoothing problem for graph neural networks from the topological view[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2020, 34(4): 3438-3445.
[6] XU K, HU W H, LESKOVEC J, et al. How powerful are graph neural networks?[EB/OL]. [2023-11-03]. https://arxiv.org/abs/1810.00826.
[7] RENDLE S, FREUDENTHALER C, SCHMIDT-THIEME L. Factorizing personalized Markov chains for next-basket recommendation[C]//Proceedings of the 19th International Conference on World Wide Web. New York: ACM, 2010: 811-820.
[8] WANG P F, GUO J F, LAN Y Y, et al. Learning hierarchical representation model for nextbasket recommendation[C]//Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2015: 403-412.
[9] RAVANMEHR R, MOHAMADREZAEI R. Introduction to session-based recommender systems[M]//Session-based recom-mender systems using deep learning. Cham: Springer, 2023: 1-26.
[10] WU L, HE X N, WANG X, et al. A survey on accuracy-oriented neural recommendation: from collaborative filtering to information-rich recommendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 35(5): 4425-4445.
[11] WANG S J, ZHANG Q, HU L, et al. Sequential/session-based recommendations: challenges, approaches, applications and opportunities[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 3425-3428.
[12] HIDASI B, KARATZOGLOU A, BALTRUNAS L, et al. Session-based recommendations with recurrent neural networks[EB/OL]. [2023-11-03]. https://arxiv.org/abs/1511.06939.
[13] LI J, REN P J, CHEN Z M, et al. Neural attentive session-based recommendation[C]//Proceedings of the 2017 ACM Conference on Information and Knowledge Management. New York: ACM, 2017: 1419-1428.
[14] QUADRANA M, KARATZOGLOU A, HIDASI B, et al. Personalizing session-based recommendations with hierarchical recurrent neural networks[C]//Proceedings of the 11th ACM Conference on Recommender Systems. New York: ACM, 2017: 130-137.
[15] 朱志国, 李伟玥, 姜盼, 等. 图神经网络会话推荐系统综述[J]. 计算机工程与应用, 2023, 59(5): 55-69.
ZHU Z G, LI W Y, JIANG P, et al. Survey of graph neural networks in session recommender systems[J]. Computer Engineering and Applications, 2023, 59(5): 55-69.
[16] WU S W, SUN F, ZHAN W T, et al. Graph neural networks in recommender systems: a survey[J]. ACM Computing Surveys, 2022, 55(5): 1-37.
[17] WU S, TANG Y Y, ZHU Y Q, et al. Session-based recommendation with graph neural networks[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2019, 33(1): 346-353.
[18] XU C F, ZHAO P P, LIU Y C, et al. Graph contextualized self-attention network for session-based recommendation[C]//Proceedings of the 28th International Joint Conference on Artificial Intelligence. Menlo Park: AAAI, 2019: 3940-3946.
[19] WANG Z Y, WEI W, CONG G, et al. 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. New York: ACM, 2020: 169-178.
[20] YU F, ZHU Y Q, LIU Q, et al. 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. New York: ACM, 2020: 1921-1924.
[21] XU G N, YANG J Y, GUO J J, et al. Int-GNN: a user intention aware graph neural network for session-based recommendation[C]//Proceedings of the 2023 IEEE International Conference on Acoustics, Speech and Signal Processing. Piscataway: IEEE, 2023: 1-5.
[22] YU J L, YIN H Z, XIA X, et al. Self-supervised learning for recommender systems: a survey[J]. IEEE Transactions on Knowledge and Data Engineering, 2024, 36(1): 335-355.
[23] HUAN C, WANG X, HE X N, et al. Self-supervised learning for recommender system[C]//Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2022: 3440-3443.
[24] XIE X, SUN F, LIU Z Y, et al. Contrastive learning for sequential recommendation[C]//Proceedings of the 2022 IEEE 38th International Conference on Data Engineering. Piscataway: IEEE, 2022: 1259-1273.
[25] XIA X, YIN H Z, YU J L, et al. Self-supervised hypergraph convolutional networks for session-based recommendation[J]. Proceedings of the AAAI Conference on Artificial Intelligence, 2021, 35(5): 4503-4511.
[26] XIA X, YIN H Z, YU J L, et al. Self-supervised graph co-training for session-based recommendation[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. New York: ACM, 2021: 2180-2190.
[27] ZENG B Q, CHI J L, HONG P L, et al. Context-aware graph embedding with gate and attention for session-based recommendation[J]. Neurocomputing, 2024, 574: 127221.
[28] CHEN Y H, HUANG L, WANG C D, et al. Hybrid-order gated graph neural network for session-based recommendation[J]. IEEE Transactions on Industrial Informatics, 2021, 18(3): 1458-1467.
[29] LIU X, ZHANG F J, HOU Z Y, et al. Self-supervised learning: generative or contrastive[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(1): 857-876.
[30] ZHANG X K, LIN H F, XU B, et al. Dynamic intent-aware iterative denoising network for session-based recommendation[J]. Information Processing & Management, 2022, 59(3): 102936.
[31] OORD A V D, LI Y Z, VINYALS O. Representation learning with contrastive predictive coding[EB/OL]. [2023-11-03]. https://arxiv.org/abs/1807.03748.
[32] LIU Q, ZENG Y F, MOKHOSI R, et al. STAMP: short-term attention/memory priority model for session-based recommendation[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2018: 1831-1839.
[33] GUPTA P, GARG D, MALHOTRA P, et al. NISER: normalized item and session representations with graph neural networks[EB/OL]. [2023-11-03]. https://arxiv.org/abs/1909.04276.
[34] CHEN T W, WONG R C W. Handling information loss of graph neural networks for session-based recommendation[C]//Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. New York: ACM, 2020: 1172-1180.