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Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation

文献类型：期刊论文

作者Zhu, Xiaofei6; Tang, Gu6; Wang, Pengfei5; Li, Chenliang4; Guo, Jiafeng3; Dietze, Stefan1,2
刊名INFORMATION SCIENCES
出版日期2023-05-01
卷号624页码:324-343
关键词Recommendation system Session-based recommendation Graph neural network Behavior modeling Attention model Representation learning
ISSN号0020-0255
DOI10.1016/j.ins.2022.10.025
英文摘要Session-based recommendation is a challenging task, which aims at making recommenda-tion for anonymous users based on in-session data, i.e. short-term interaction data. Most session-based recommendation methods only model user's preferences with the current session sequence, which ignore rich information from a global perspective. Meanwhile, previous works usually apply GNN to capture the transformation relationship between items, however the graph used in GNN is built through a static mode, which may introduce noise to the graph structure if user's preferences shift. In this paper, we propose a novel method called Dynamic Global Structure Enhanced Multi-channel Graph Neural Network (DGS-MGNN) to learn accurate representations of items from multiple perspectives. In DGS-MGNN, we propose a novel GNN model named Multi-channel Graph Neural Network to generate the local, global and consensus graphs dynamically and learn more informative representations of items based on the corresponding graph. Meanwhile, in order to reduce the noise information within sessions, we utilize the graph structure to assist the attention mechanism to filter noisy information within each session, so as to gen-erate an accurate intention representation for the user. Finally, combined with a repeat and explore module, a more accurate prediction probability distribution is generated. We con-duct extensive experiments on three widely used datasets, and the results demonstrate that DGS-MGNN is consistently superior to the state-of-the-art baseline models. (c) 2022 Published by Elsevier Inc.
资助项目National Natural Science Foundation of China[62141201] ; Major Project of Science and Technology Research Program of Chongqing Education Commission of China[KJZD-M202201102] ; Federal Ministry of Education and Research[01IS21086]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000915813400001
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/20000]  
专题中国科学院计算技术研究所期刊论文
通讯作者Zhu, Xiaofei
作者单位1.Heinrich Heine Univ Dusseldorf, Inst Comp Sci, D-40225 Dusseldorf, Germany
2.Leibniz Inst Social Sci, Knowledge Technol Social Sci, D-50667 Cologne, Germany
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.Wuhan Univ, Sch Cyber Sci & Engn, Wuhan 430072, Peoples R China
5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
6.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China
推荐引用方式
GB/T 7714		 Zhu, Xiaofei,Tang, Gu,Wang, Pengfei,et al. Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation[J]. INFORMATION SCIENCES,2023,624:324-343.
APA Zhu, Xiaofei,Tang, Gu,Wang, Pengfei,Li, Chenliang,Guo, Jiafeng,&Dietze, Stefan.(2023).Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation.INFORMATION SCIENCES,624,324-343.
MLA Zhu, Xiaofei,et al."Dynamic global structure enhanced multi-channel graph neural network for session-based recommendation".INFORMATION SCIENCES 624(2023):324-343.

入库方式： OAI收割

来源：计算技术研究所

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