Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation
Existing multi-behavior sequential recommendation methods obtain users’ interest preferences by analyzing their historical multi-behavior information to uncover users’ potential intentions in multi-behavior sequential recommendation. However, the existing methods still have pro...
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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/10786978/ |
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| author | Sitong Yan Chao Zhao Ningning Shen Shaopeng Jiang |
| author_facet | Sitong Yan Chao Zhao Ningning Shen Shaopeng Jiang |
| author_sort | Sitong Yan |
| collection | DOAJ |
| description | Existing multi-behavior sequential recommendation methods obtain users’ interest preferences by analyzing their historical multi-behavior information to uncover users’ potential intentions in multi-behavior sequential recommendation. However, the existing methods still have problems such as unstable users’ interest preferences and difficulty in capturing the fine-grained relationships between behaviors. This paper proposes a framework called Position-aware Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation (PHCL-MBSR). It introduces position context encoding in the temporal dimension, focusing on the context behavior dependencies. In the spatial dimension, a global hypergraph is constructed to capture the high-order relationships between sequences, and the global multi-behavior dependencies in the spatial dimension are captured through hypergraph contrastive learning. PHCL-MBSR has been experimentally evaluated on three benchmark datasets, and the results demonstrate the effectiveness and interpretability of this framework. |
| format | Article |
| id | doaj-art-3db3035b118f4902a171038ee07d3d40 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-3db3035b118f4902a171038ee07d3d402025-08-20T01:58:00ZengIEEEIEEE Access2169-35362024-01-011218595818597010.1109/ACCESS.2024.351398210786978Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence RecommendationSitong Yan0https://orcid.org/0009-0007-8461-5334Chao Zhao1https://orcid.org/0000-0002-0736-7898Ningning Shen2Shaopeng Jiang3https://orcid.org/0009-0009-1508-0422School of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaSchool of Information and Electrical Engineering, Hebei University of Engineering, Handan, ChinaExisting multi-behavior sequential recommendation methods obtain users’ interest preferences by analyzing their historical multi-behavior information to uncover users’ potential intentions in multi-behavior sequential recommendation. However, the existing methods still have problems such as unstable users’ interest preferences and difficulty in capturing the fine-grained relationships between behaviors. This paper proposes a framework called Position-aware Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation (PHCL-MBSR). It introduces position context encoding in the temporal dimension, focusing on the context behavior dependencies. In the spatial dimension, a global hypergraph is constructed to capture the high-order relationships between sequences, and the global multi-behavior dependencies in the spatial dimension are captured through hypergraph contrastive learning. PHCL-MBSR has been experimentally evaluated on three benchmark datasets, and the results demonstrate the effectiveness and interpretability of this framework.https://ieeexplore.ieee.org/document/10786978/Hypergraph neural networksequential recommendationmulti-behavior recommendationcontrastive learning |
| spellingShingle | Sitong Yan Chao Zhao Ningning Shen Shaopeng Jiang Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation IEEE Access Hypergraph neural network sequential recommendation multi-behavior recommendation contrastive learning |
| title | Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation |
| title_full | Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation |
| title_fullStr | Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation |
| title_full_unstemmed | Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation |
| title_short | Position-Awareness and Hypergraph Contrastive Learning for Multi-Behavior Sequence Recommendation |
| title_sort | position awareness and hypergraph contrastive learning for multi behavior sequence recommendation |
| topic | Hypergraph neural network sequential recommendation multi-behavior recommendation contrastive learning |
| url | https://ieeexplore.ieee.org/document/10786978/ |
| work_keys_str_mv | AT sitongyan positionawarenessandhypergraphcontrastivelearningformultibehaviorsequencerecommendation AT chaozhao positionawarenessandhypergraphcontrastivelearningformultibehaviorsequencerecommendation AT ningningshen positionawarenessandhypergraphcontrastivelearningformultibehaviorsequencerecommendation AT shaopengjiang positionawarenessandhypergraphcontrastivelearningformultibehaviorsequencerecommendation |