Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution
In the field of session-based recommendation by anonymous sessions, the commonly used supervised learning modeling method has the problem of sub-optimal recommendation. The supervised reinforcement learning (SRL) recommendation framework can be used to solve this problem, but there is currently a la...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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IEEE
2023-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/10286537/ |
| _version_ | 1797649816953552896 |
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| author | Shunpan Liang Guozheng Zhang Wenhui Ren |
| author_facet | Shunpan Liang Guozheng Zhang Wenhui Ren |
| author_sort | Shunpan Liang |
| collection | DOAJ |
| description | In the field of session-based recommendation by anonymous sessions, the commonly used supervised learning modeling method has the problem of sub-optimal recommendation. The supervised reinforcement learning (SRL) recommendation framework can be used to solve this problem, but there is currently a lack of research on graph state representations of anonymous sessions in this field. In this regard, we propose a supervised reinforcement session recommendation model, HG-SRL, based on global hypergraphs and local session graphs. In this model, we propose, for the first time, a state representation method based on hypergraph neural networks and graph neural networks to bridge the gap in SRL in graph state construction methods. To fully utilize graph information at both global and local levels, we propose a self-matching attention fusion mechanism, which fuses the different levels of information contained in the two graphs through cross-calculation and then embeds them in the final graph state representation. To make the state more comprehensive, we also mine the neighboring sessions of each anonymous session on the global session hypergraph to supplement the neighboring session information of the anonymous session. Experimental tests were conducted on three real-world datasets, which have shown that HG-SRL can effectively improve the accuracy of session recommendations. |
| first_indexed | 2024-03-11T15:51:28Z |
| format | Article |
| id | doaj.art-f76097236b834afbb4eba203e0b4dee8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-03-11T15:51:28Z |
| publishDate | 2023-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-f76097236b834afbb4eba203e0b4dee82023-10-25T23:00:19ZengIEEEIEEE Access2169-35362023-01-011111538011539110.1109/ACCESS.2023.332533310286537Supervised Reinforcement Session Recommendation Model Based on Dual-Graph ConvolutionShunpan Liang0https://orcid.org/0000-0002-2015-7000Guozheng Zhang1Wenhui Ren2School of Information Science and Engineering, Xinjiang University of Science and Technology, Xinjiang, Korla, ChinaSchool of Information Science and Engineering, Yanshan University, Hebei, Qinhuangdao, ChinaSchool of Information Science and Engineering, Yanshan University, Hebei, Qinhuangdao, ChinaIn the field of session-based recommendation by anonymous sessions, the commonly used supervised learning modeling method has the problem of sub-optimal recommendation. The supervised reinforcement learning (SRL) recommendation framework can be used to solve this problem, but there is currently a lack of research on graph state representations of anonymous sessions in this field. In this regard, we propose a supervised reinforcement session recommendation model, HG-SRL, based on global hypergraphs and local session graphs. In this model, we propose, for the first time, a state representation method based on hypergraph neural networks and graph neural networks to bridge the gap in SRL in graph state construction methods. To fully utilize graph information at both global and local levels, we propose a self-matching attention fusion mechanism, which fuses the different levels of information contained in the two graphs through cross-calculation and then embeds them in the final graph state representation. To make the state more comprehensive, we also mine the neighboring sessions of each anonymous session on the global session hypergraph to supplement the neighboring session information of the anonymous session. Experimental tests were conducted on three real-world datasets, which have shown that HG-SRL can effectively improve the accuracy of session recommendations.https://ieeexplore.ieee.org/document/10286537/Session recommendationreinforcement learninghypergraph neural network |
| spellingShingle | Shunpan Liang Guozheng Zhang Wenhui Ren Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution IEEE Access Session recommendation reinforcement learning hypergraph neural network |
| title | Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution |
| title_full | Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution |
| title_fullStr | Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution |
| title_full_unstemmed | Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution |
| title_short | Supervised Reinforcement Session Recommendation Model Based on Dual-Graph Convolution |
| title_sort | supervised reinforcement session recommendation model based on dual graph convolution |
| topic | Session recommendation reinforcement learning hypergraph neural network |
| url | https://ieeexplore.ieee.org/document/10286537/ |
| work_keys_str_mv | AT shunpanliang supervisedreinforcementsessionrecommendationmodelbasedondualgraphconvolution AT guozhengzhang supervisedreinforcementsessionrecommendationmodelbasedondualgraphconvolution AT wenhuiren supervisedreinforcementsessionrecommendationmodelbasedondualgraphconvolution |