Intent-aware knowledge graph-based model for electrical power material recommendation
In the field of electrical power material management, it is paramount that users receive accurate recommendations regarding the electrical power materials they require. Recently, a growing number of studies have been dedicated to graph neural network (GNN)-based recommendation systems due to their a...
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2025-07-01
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| Series: | PeerJ Computer Science |
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| Online Access: | https://peerj.com/articles/cs-3023.pdf |
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| _version_ | 1839599374469955584 |
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| author | Lin Zhao Ning Luan Weihua Cheng Shuming Feng Hui Wang Yongcheng Yang Guixiang Zhu |
| author_facet | Lin Zhao Ning Luan Weihua Cheng Shuming Feng Hui Wang Yongcheng Yang Guixiang Zhu |
| author_sort | Lin Zhao |
| collection | DOAJ |
| description | In the field of electrical power material management, it is paramount that users receive accurate recommendations regarding the electrical power materials they require. Recently, a growing number of studies have been dedicated to graph neural network (GNN)-based recommendation systems due to their ability to seamlessly combine node information with topological structure, enhancing the effectiveness of recommendations. However, a notable drawback of current GNN-based recommendation is their inability to explicitly capture users’ intent in recommendations, which limits the performance. In fact, users’ intent is crucial in determining their actions. One example is when users first form an intent to buy a particular set of items and then choose a specific item from the set based on their preferences. To fill this gap, this article proposes an intent-aware knowledge graph-based model for electrical material recommendation, named IKG-EMR. IKG-EMR models user preferences and intent by leveraging knowledge graph and user behavior sequences, respectively. Specifically, a graph neural network is adopted to generate user intent embedding and item embedding from the tripartite graph of “User-Item-Topic”, and a multi-head attention network (Transformer) is used for extracting preference from user behavior sequences. Finally, an adaptive fusion with attention network is devised to generate comprehensive user representation by integrating user preference and intent features. Extensive experiments conducted on the real-life electric power materials show that our proposed model outperforms state-of-the-art methods. |
| format | Article |
| id | doaj-art-e80e37f91b424b16b09f7c33b29cf6ba |
| institution | Matheson Library |
| issn | 2376-5992 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | PeerJ Inc. |
| record_format | Article |
| series | PeerJ Computer Science |
| spelling | doaj-art-e80e37f91b424b16b09f7c33b29cf6ba2025-08-02T15:05:16ZengPeerJ Inc.PeerJ Computer Science2376-59922025-07-0111e302310.7717/peerj-cs.3023Intent-aware knowledge graph-based model for electrical power material recommendationLin Zhao0Ning Luan1Weihua Cheng2Shuming Feng3Hui Wang4Yongcheng Yang5Guixiang Zhu6Jiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaJiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaJiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaJiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaJiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaJiangsu Electric Power Information Technology Co. Ltd, Nanjing, ChinaNanjing University of Finance and Economics, Nanjing, ChinaIn the field of electrical power material management, it is paramount that users receive accurate recommendations regarding the electrical power materials they require. Recently, a growing number of studies have been dedicated to graph neural network (GNN)-based recommendation systems due to their ability to seamlessly combine node information with topological structure, enhancing the effectiveness of recommendations. However, a notable drawback of current GNN-based recommendation is their inability to explicitly capture users’ intent in recommendations, which limits the performance. In fact, users’ intent is crucial in determining their actions. One example is when users first form an intent to buy a particular set of items and then choose a specific item from the set based on their preferences. To fill this gap, this article proposes an intent-aware knowledge graph-based model for electrical material recommendation, named IKG-EMR. IKG-EMR models user preferences and intent by leveraging knowledge graph and user behavior sequences, respectively. Specifically, a graph neural network is adopted to generate user intent embedding and item embedding from the tripartite graph of “User-Item-Topic”, and a multi-head attention network (Transformer) is used for extracting preference from user behavior sequences. Finally, an adaptive fusion with attention network is devised to generate comprehensive user representation by integrating user preference and intent features. Extensive experiments conducted on the real-life electric power materials show that our proposed model outperforms state-of-the-art methods.https://peerj.com/articles/cs-3023.pdfRecommender systemGraph neural networksTopic modelTransformer |
| spellingShingle | Lin Zhao Ning Luan Weihua Cheng Shuming Feng Hui Wang Yongcheng Yang Guixiang Zhu Intent-aware knowledge graph-based model for electrical power material recommendation PeerJ Computer Science Recommender system Graph neural networks Topic model Transformer |
| title | Intent-aware knowledge graph-based model for electrical power material recommendation |
| title_full | Intent-aware knowledge graph-based model for electrical power material recommendation |
| title_fullStr | Intent-aware knowledge graph-based model for electrical power material recommendation |
| title_full_unstemmed | Intent-aware knowledge graph-based model for electrical power material recommendation |
| title_short | Intent-aware knowledge graph-based model for electrical power material recommendation |
| title_sort | intent aware knowledge graph based model for electrical power material recommendation |
| topic | Recommender system Graph neural networks Topic model Transformer |
| url | https://peerj.com/articles/cs-3023.pdf |
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