Multimodal fusion framework based on knowledge graph for personalized recommendation
Article
Wang, Jingjing, Xie, Haoran, Zhang, Siyu, Qin, S. Joe, Tao, Xiaohui, Wang, Fu Lee and Xu, Xiaoliang. 2025. "Multimodal fusion framework based on knowledge graph for personalized recommendation." Expert Systems with Applications. 268. https://doi.org/10.1016/j.eswa.2024.126308
Article Title | Multimodal fusion framework based on knowledge graph for personalized recommendation |
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ERA Journal ID | 17852 |
Article Category | Article |
Authors | Wang, Jingjing, Xie, Haoran, Zhang, Siyu, Qin, S. Joe, Tao, Xiaohui, Wang, Fu Lee and Xu, Xiaoliang |
Journal Title | Expert Systems with Applications |
Journal Citation | 268 |
Article Number | 126308 |
Number of Pages | 9 |
Year | 2025 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0957-4174 |
1873-6793 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.eswa.2024.126308 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0957417424031750 |
Abstract | Knowledge Graphs (KGs), which contain a wealth of knowledge, have been commonly employed in recommendation systems as a valuable knowledge-driven tool for supporting high-quality representations. To further enhance the model’s ability to understand the real world, Multimodal Knowledge Graphs (MKGs) are proposed to extract rich knowledge and facts among objects from text and visual content. However, existing MKG-based methods primarily focus on the reasoning relationships between entities by utilizing multimodal information as auxiliary data in the KG while overlooking the interactions between modalities. In this paper, we propose a Multimodal fusion framework based on Knowledge Graph for personalized Recommendation (Multi-KG4Rec) to address these limitations. Specifically, we systematically analyze the shortcomings of existing multimodal graph construction methods. To this end, we propose a modal fusion module to extract the user modal preference at a fine-grained level. Furthermore, we conduct extensive experiments on two real-world datasets from different domains to evaluate the performance of our model, and the results demonstrate the efficiency of the Multi-KG4Rec. |
Keywords | Knowledge graphs; Multimodal fusion framework; Recommender system |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 460206. Knowledge representation and reasoning |
Public Notes | Files associated with this item cannot be displayed due to copyright restrictions. |
Byline Affiliations | Hangzhou Dianzi University, China |
Lingnan University of Hong Kong, China | |
University of Southern Queensland | |
Hong Kong Metropolitan University, China |
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https://research.usq.edu.au/item/zx1v3/multimodal-fusion-framework-based-on-knowledge-graph-for-personalized-recommendation
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