Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation
Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constru...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-08-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/13/15/2496 |
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| author | Bingchen Liu Guangyuan Dong Zihao Li Yuanyuan Fang Jingchen Li Wenqi Sun Bohan Zhang Changzhi Li Xin Li |
| author_facet | Bingchen Liu Guangyuan Dong Zihao Li Yuanyuan Fang Jingchen Li Wenqi Sun Bohan Zhang Changzhi Li Xin Li |
| author_sort | Bingchen Liu |
| collection | DOAJ |
| description | Knowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results. |
| format | Article |
| id | doaj-art-7dcd5abaa12f44c6a3a181b3abdd46e7 |
| institution | Kabale University |
| issn | 2227-7390 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj-art-7dcd5abaa12f44c6a3a181b3abdd46e72025-08-20T03:36:31ZengMDPI AGMathematics2227-73902025-08-011315249610.3390/math13152496Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for RecommendationBingchen Liu0Guangyuan Dong1Zihao Li2Yuanyuan Fang3Jingchen Li4Wenqi Sun5Bohan Zhang6Changzhi Li7Xin Li8School of Software, Shandong University, Jinan 250101, ChinaDepartment of Statistics and Data Science, National University of Singapore, Singapore 119077, SingaporeSchool of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215028, ChinaMetropolitan College, Boston University, Boston, MA 02215, USASchool of Software, Shandong University, Jinan 250101, ChinaSchool of Computer and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250020, ChinaCollege of Electronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, ChinaIEIT SYSTEMS Co., Ltd., Jinan 250000, ChinaSchool of Software, Shandong University, Jinan 250101, ChinaKnowledge-graph-based recommendation aims to provide personalized recommendation services to users based on their historical interaction information, which is of great significance for shopping transaction rates and other aspects. With the rapid growth of online shopping, the knowledge graph constructed from users’ historical interaction data now incorporates multiattribute information, including timestamps, images, and textual content. The information of multiple modalities is difficult to effectively utilize due to their different representation structures and spaces. The existing methods attempt to utilize the above information through simple embedding representation and aggregation, but ignore targeted representation learning for information with different attributes and learning effective weights for aggregation. In addition, existing methods are not sufficient for effectively modeling temporal information. In this article, we propose MTR, a knowledge graph recommendation framework based on mixture of experts network. To achieve this goal, we use a mixture-of-experts network to learn targeted representations and weights of different product attributes for effective modeling and utilization. In addition, we effectively model the temporal information during the user shopping process. A thorough experimental study on popular benchmarks validates that MTR can achieve competitive results.https://www.mdpi.com/2227-7390/13/15/2496knowledge graphmixture of expertsmultimodal knowledge graph |
| spellingShingle | Bingchen Liu Guangyuan Dong Zihao Li Yuanyuan Fang Jingchen Li Wenqi Sun Bohan Zhang Changzhi Li Xin Li Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation Mathematics knowledge graph mixture of experts multimodal knowledge graph |
| title | Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation |
| title_full | Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation |
| title_fullStr | Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation |
| title_full_unstemmed | Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation |
| title_short | Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation |
| title_sort | multimodal temporal knowledge graph embedding method based on mixture of experts for recommendation |
| topic | knowledge graph mixture of experts multimodal knowledge graph |
| url | https://www.mdpi.com/2227-7390/13/15/2496 |
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