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CMC-GCN: Consistent multi-granularity cascading graph convolution network for multi-behavior recommendation

文献类型：期刊论文

作者Yin, Yabo1; Zhu, Xiaofei1; Huang, Kunyang2; Wang, Wenshan3; Zhang, Yihao4; Wang, Pengfei5; Fan, Yixing3; Guo, Jiafeng3
刊名NEUROCOMPUTING
出版日期2025-10-28
卷号651页码:12
关键词Multi-behavior recommendation Self-supervised learning Graph convolutional network
ISSN号0925-2312
DOI10.1016/j.neucom.2025.130952
英文摘要Multi-behavior recommendation (MBR) has garnered growing attention recently due to its ability to mitigate the sparsity issue by inferring user preferences from various auxiliary behaviors to improve predictions for the target behavior. Although existing research on MBR has yielded impressive results, it still faces two major limitations. First, previous methods mainly focus on modeling fine-grained interaction information between users and items under each behavior, which may suffer from sparsity issue. Second, existing models usually concentrate on exploiting dependencies between two consecutive behaviors, leaving intra-and inter-behavior consistency largely unexplored. To this end, we propose a novel approach named Consistent Multi-Granularity Cascading Graph Convolution Network for Multi-Behavior Recommendation (CMC-GCN). To be specific, we first explore both fine-and coarse-grained correlations among users or items of each behavior by simultaneously modeling the behavior-specific interaction graph and its corresponding hypergraph in a cascaded manner. Then, we propose a behavior consistency-guided alignment strategy that preserves consistent representations between the interaction graph and its associated hypergraph for each behavior, while also maintaining representation consistency across different behaviors. Extensive experiments and analyses on four real-world benchmark datasets demonstrate that our proposed approach is consistently superior to previous state-of-the-art methods due to its capability to effectively attenuate the sparsity issue as well as keep both intra-and inter-behavior consistencies. The code is available at https://github.com/marqu22/CMCGCN.git.
资助项目National Natural Science Foundation of China[62472059] ; National Natural Science Foundation of China[62372059] ; Natural Science Foundation of Chongqing, China[CSTB2022NSCQ-MSX1672] ; Chongqing Talent Plan Project, China[CSTC2024YCJH-BGZXM0022] ; Major Project of Science and Technology Research Program of Chongqing Education Commission of China[KJZD-M202201102]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:001548233600007
出版者ELSEVIER
源URL[http://119.78.100.204/handle/2XEOYT63/41978]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhu, Xiaofei
作者单位1.Chongqing Univ Technol, Coll Comp Sci & Engn, Chongqing 400054, Peoples R China
2.Wenzhou Kean Univ, Coll Sci Math & Technol, Wenzhou 325060, Peoples R China
3.Chinese Acad Sci, Inst Comp Technol, Beijing 100190, Peoples R China
4.Chongqing Univ Technol, Sch Artificial Intelligence, Chongqing 400054, Peoples R China
5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China
推荐引用方式
GB/T 7714		 Yin, Yabo,Zhu, Xiaofei,Huang, Kunyang,et al. CMC-GCN: Consistent multi-granularity cascading graph convolution network for multi-behavior recommendation[J]. NEUROCOMPUTING,2025,651:12.
APA Yin, Yabo.,Zhu, Xiaofei.,Huang, Kunyang.,Wang, Wenshan.,Zhang, Yihao.,...&Guo, Jiafeng.(2025).CMC-GCN: Consistent multi-granularity cascading graph convolution network for multi-behavior recommendation.NEUROCOMPUTING,651,12.
MLA Yin, Yabo,et al."CMC-GCN: Consistent multi-granularity cascading graph convolution network for multi-behavior recommendation".NEUROCOMPUTING 651(2025):12.

入库方式： OAI收割

来源：计算技术研究所

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