基于共现关系重构的超图增强会话推荐模型
A Hypergraph-Enhanced Session Recommendation Model Based on Co-Occurrence Relationship Reconstruction
摘要: 会话推荐旨在基于匿名用户的历史行为序列预测下一个最有可能交互的项目。现有的会话推荐模型大多数直接将会话建模成序列，只考虑图模型结构中的单层信息，从而忽略了成对项目间的共现关系及其存在的信息转移差异。本文提出一种基于共现关系重构的超图增强会话推荐模型(CR-HGNN)，在充分考虑项目间共现关系的基础上，将会话序列建模成全局超图和局部图。该模型使用超图卷积神经网络捕获全局级会话信息，同时使用共现重构的方式对成对项目间的转移关系加以区分以提取局部级会话信息。最后，使用加和池化操作融合全局级信息与局部级信息，同时用标签平滑操作得到最终的项目表示以此来预测下一个最有可能交互的项目。在三个真实公开的数据集上做了大量的实验，实验结果表明，在建模时关注项目间的共现关系能够充分捕获项目的转移信息差异，一定程度上提升了本文提出的CR-HGNN模型的推荐性能，具体表现在P@20、MRR@20等指标上均优于基线模型。
Abstract: Session recommendation aims to predict the next most likely item to be interacted with based on the historical behavior sequence of anonymous users. Most of the existing session recommendation models directly model sessions as sequences and consider only the single layer of information in the graph model structure, thus ignoring the co-occurrence relationship and the existence of information transfer differences between pairs of items. In this paper, we propose a hypergraph-enhanced session recommendation model (CR-HGNN) based on co-occurrence relationship reconstruction, which models session sequences as global hypergraphs and local graphs on the basis of fully considering the co-occurrence relationship between items. The model uses a hypergraph convolutional neural network to capture global-level session information, while co-occur- rence reconstruction is used to differentiate transfer relationships between pairs of items to extract local-level session information. Finally, the sumpooling operation is used to fuse the global-level information with the local-level information, and the label smoothing operation is used to obtain the final item representation to predict the next most likely interacting item. Extensive experiments on three real public datasets show that focusing on the co-occurrence relationship between items in modelling can fully capture the transfer information differences of items, which to some extent improves the recommendation performance of the CR-HGNN model proposed in this paper, which outperforms the baseline model in terms of the metrics such as P@20 and MRR@20.
文章引用：王益飞, 翟雨欣. 基于共现关系重构的超图增强会话推荐模型[J]. 建模与仿真, 2024, 13(3): 2543-2557. https://doi.org/10.12677/mos.2024.133232

参考文献

[1] Yang, C.C., Chen, H. and Hong, K. (2003) Visualization of Large Category Map for Internet Browsing. Decision Support Systems, 35, 89-102. [Google Scholar] [CrossRef] 
[2] Sarwar, B., Karypis, G., Konstan, J., et al. (2001) Item-Based Collaborative Filtering Recommendation Algorithms. Proceedings of the 10th International Conference on World Wide Web, Hong Kong, 1-5 May 2001, 285-295. [Google Scholar] [CrossRef] 
[3] Koren, Y., Bell, R. and Volinsky, C. (2009) Matrix Factorization Techniques for Recommender Systems. Computer, 42, 30-37. [Google Scholar] [CrossRef] 
[4] Rendle, S., Freudenthaler, C. and Schmidt-Thieme, L. (2010) Factorizing Personalized Markov Chains for Next-Basket Recommendation. Proceedings of the 19th International Conference on World Wide Web, Raleigh, 26-30 April 2010, 811-820. [Google Scholar] [CrossRef] 
[5] Cho, K., Van Merriënboer, B., Gulcehre, C., et al. (2014) Learning Phrase Representations Using RNN Encoder-Decoder for Statistical Machine Translation. arXiv: 1406.1078. [Google Scholar] [CrossRef] 
[6] Scarselli, F., Gori, M., Tsoi, A.C., et al. (2008) The Graph Neural Network Model. IEEE Transactions on Neural Networks, 20, 61-80. [Google Scholar] [CrossRef] 
[7] Yu, B., Zhang, Y., Xie, Y., et al. (2021) Influence-Aware Graph Neural Networks. Applied Soft Computing, 104, Article ID: 107169. [Google Scholar] [CrossRef] 
[8] Hidasi, B., Karatzoglou, A., Baltrunas, L., et al. (2016) Session-Based Recommendations with Recurrent Neural Networks. arXiv: 1511.06939.
[9] Li, J., Ren, P., Chen, Z., et al. (2017) Neural Attentive Session-Based Recommendation. Proceedings of the ACM on Conference on Information and Knowledge Management, Singapore, 6-10 November 2017, 1419-1428. [Google Scholar] [CrossRef] 
[10] Liu, Q., Zeng, Y., Mokhosi, R., et al. (2018) STAMP: Short-Term Attention Memory Priority Model for Session-Based Recommendation. Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, London, 19-23 August 2018, 1831-1839. [Google Scholar] [CrossRef] 
[11] Wu, S., Tang, Y., Zhu, Y., et al. (2019) Session-Based Recommendation with Graph Neural Networks. Proceedings of the AAAI Conference on Artificial Intelligence, 33, 346-353. [Google Scholar] [CrossRef] 
[12] Qiu, R., Li, J., Huang, Z., et al. (2019) Rethinking the Item Order in Session-Based Recommendation with Graph Neural Networks. Proceedings of the 28th ACM International Conference on Information and Knowledge Management, Beijing, 3-7 November 2019, 579-588. [Google Scholar] [CrossRef] 
[13] Xu, C., Zhao, P., Liu, Y., et al. (2019) Graph Contextualized Self-Attention Network for Session-Based Recommendation. Proceedings of the 28th International Joint Conference on Artificial Intelligence, Macao, 10-16 August 2019, 3940-3946. [Google Scholar] [CrossRef] 
[14] Xia, X., Yin, H., Yu, J., et al. (2021) Self-Supervised Hypergraph Convolutional Networks for Session-Based Recommendation. Proceedings of the AAAI Conference on Artificial Intelligence, 35, 4503-4511. [Google Scholar] [CrossRef] 
[15] Wang, S., Cao, L., Wang, Y., et al. (2021) A Survey on Session-Based Recommender Systems. ACM Computing Surveys, 54, 1-38. [Google Scholar] [CrossRef] 
[16] Song, J., Shen, H., Ou, Z., Zhang, J., Xiao, T. and Liang, S. (2019) ISLF: Interest Shift and Latent Factors Combination Model for Session-Based Recommendation. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence, Macao, 10-16 August 2019, 5765-5771. [Google Scholar] [CrossRef] 
[17] Pan, Z., Cai, F., Chen, W., et al. (2020) Star Graph Neural Networks for Session-Based Recommendation. Proceedings of the 29th ACM International Conference on Information & Knowledge Management, 19-23 October 2020, 1195-1204. [Google Scholar] [CrossRef] 
[18] Wang, Z.Y., Wei, W., et al. (2020) Global Context Enhanced Graph Neural Networks for Session-Based Recommendation. Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 25-30 July 2020, 169-178. [Google Scholar] [CrossRef] 
[19] Han, Q., Zhang, C., Chen, R., et al. (2022) Multi-Faceted Global Item Relation Learning for Session-Based Recommendation. Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, 11-15 July 2022, 1705-1715. [Google Scholar] [CrossRef] 
[20] Pang, Y., Wu, L., Shen, Q., et al. (2022) Heterogeneous Global Graph Neural Networks for Personalized Session-Based Recommendation. Proceedings of the 15th ACM International Conference on Web Search and Data Mining, New York, 21-25 February 2022, 775-783. [Google Scholar] [CrossRef] 
[21] Cai, Y. and Li, J. (2022) Rethinking Transition Relationship between Co-Occurring Items in Graph Neural Networks for Session-Based Recommendation. Applied Soft Computing, 126, Article ID: 109231. [Google Scholar] [CrossRef] 
[22] Sheng, Z., Zhang, T., Zhang, Y., et al. (2023) Enhanced Graph Neural Network for Session-Based Recommendation. Expert Systems with Applications, 213, Article ID: 118887. [Google Scholar] [CrossRef] 
[23] Gwadabe, T.R. and Liu, Y. (2022) IC-GAR: Item Co-Occurrence Graph Augmented Session-Based Recommendation. Neural Computing and Applications, 34, 7581-7596. [Google Scholar] [CrossRef] 
[24] Chen, Y., Tang, Y. and Yuan, Y. (2023) Attention-Enhanced Graph Neural Networks with Global Context for Session-Based Recommendation. IEEE Access, 11, 26237-26246. [Google Scholar] [CrossRef] 
[25] Vaswani, A., Shazeer, N., Parmar, N., et al. (2017) Attention Is All You Need. Advances in Neural Information Processing Systems, 30, 1-8.
[26] Peng, D. and Zhang, S. (2022) GC-HGNN: A Global-Context Supported Hypergraph Neural Network for Enhancing Session-Based Recommendation. Electronic Commerce Research and Applications, 52, Article ID: 101129. [Google Scholar] [CrossRef] 
[27] Zhao, C.Y., Cao, H.L., Lv, P.T., et al. (2023) Adaptive Context-Embedded Hypergraph Convolutional Network for Session-Based Recommendation. Information Technology and Control, 52, 111-127. [Google Scholar] [CrossRef] 

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