Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation
Session-Based Recommendation(SBR) aims to capture users’ short-term and dynamic preferences through anonymous sessions. Most existing SBR methods neglect the collaborative information between multiple behaviors in a session when modeling user preferences, and they often struggle to captur...
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10763488/ |
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| author | Zhen Zhang Jingai Zhang Jintao Chen Yuzhao Huang Xiaoyang Huang |
| author_facet | Zhen Zhang Jingai Zhang Jintao Chen Yuzhao Huang Xiaoyang Huang |
| author_sort | Zhen Zhang |
| collection | DOAJ |
| description | Session-Based Recommendation(SBR) aims to capture users’ short-term and dynamic preferences through anonymous sessions. Most existing SBR methods neglect the collaborative information between multiple behaviors in a session when modeling user preferences, and they often struggle to capture the complex correlations of contextual information across sessions, which can lead to poor recommendation performance. To address these issues, this paper proposes a Self-Supervised enhancement method for Multi-Behavior Session-based Recommendation(SSMB-SR). SSMB-SR represents the session sequence using a heterogeneous graph to capture intricate behavior interactions and a hypergraph for contextual information integration. Specifically, we designed a heterogeneous enhancement module that deeply understands the intrinsic connections of behaviors and the interdependencies between different behavior types by enhancing the behavioral information of the central node, effectively capturing the complex dynamic interactions between nodes within the session to obtain accurate item embeddings. Concurrently, we propose a self-supervised training method for the module that mitigates location bias and minimizes the impact of noisy behaviors. For cross-session, we combine relevant contextual information through a hypergraph to achieve accurate recommendation results. Experimental results show that our proposed self-supervised enhancement method significantly improves recommendation performance and has a better performance compared to recommendation methods that only consider a single behavior. |
| format | Article |
| id | doaj-art-2ff8b677a33c40eeba52b87bf5c9cc06 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-2ff8b677a33c40eeba52b87bf5c9cc062025-08-20T03:46:32ZengIEEEIEEE Access2169-35362024-01-011217526817527710.1109/ACCESS.2024.350449610763488Self-Supervised Enhancement Method for Multi-Behavior Session-Based RecommendationZhen Zhang0Jingai Zhang1https://orcid.org/0009-0007-7012-7540Jintao Chen2Yuzhao Huang3Xiaoyang Huang4Department of Computer Science, University of Reading, Reading, U.K.Guangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi, ChinaGuangxi Key Laboratory of Trusted Software, Guilin University of Electronic Technology, Guilin, Guangxi, ChinaGuangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, ChinaGuangxi Key Laboratory of Automatic Detecting Technology and Instruments, Guilin University of Electronic Technology, Guilin, Guangxi, ChinaSession-Based Recommendation(SBR) aims to capture users’ short-term and dynamic preferences through anonymous sessions. Most existing SBR methods neglect the collaborative information between multiple behaviors in a session when modeling user preferences, and they often struggle to capture the complex correlations of contextual information across sessions, which can lead to poor recommendation performance. To address these issues, this paper proposes a Self-Supervised enhancement method for Multi-Behavior Session-based Recommendation(SSMB-SR). SSMB-SR represents the session sequence using a heterogeneous graph to capture intricate behavior interactions and a hypergraph for contextual information integration. Specifically, we designed a heterogeneous enhancement module that deeply understands the intrinsic connections of behaviors and the interdependencies between different behavior types by enhancing the behavioral information of the central node, effectively capturing the complex dynamic interactions between nodes within the session to obtain accurate item embeddings. Concurrently, we propose a self-supervised training method for the module that mitigates location bias and minimizes the impact of noisy behaviors. For cross-session, we combine relevant contextual information through a hypergraph to achieve accurate recommendation results. Experimental results show that our proposed self-supervised enhancement method significantly improves recommendation performance and has a better performance compared to recommendation methods that only consider a single behavior.https://ieeexplore.ieee.org/document/10763488/Session-based recommendationgraph neural networksheterogeneous graphhypergraphself-supervised learning |
| spellingShingle | Zhen Zhang Jingai Zhang Jintao Chen Yuzhao Huang Xiaoyang Huang Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation IEEE Access Session-based recommendation graph neural networks heterogeneous graph hypergraph self-supervised learning |
| title | Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation |
| title_full | Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation |
| title_fullStr | Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation |
| title_full_unstemmed | Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation |
| title_short | Self-Supervised Enhancement Method for Multi-Behavior Session-Based Recommendation |
| title_sort | self supervised enhancement method for multi behavior session based recommendation |
| topic | Session-based recommendation graph neural networks heterogeneous graph hypergraph self-supervised learning |
| url | https://ieeexplore.ieee.org/document/10763488/ |
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