Multimodal Temporal Knowledge Graph Embedding Method Based on Mixture of Experts for Recommendation

Bingchen Liu, Guangyuan Dong, Zihao Li, Yuanyuan Fang, Jingchen Li, Wenqi Sun, Bohan Zhang, Changzhi Li and Xin Li ()
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Bingchen Liu: School of Software, Shandong University, Jinan 250101, China
Guangyuan Dong: Department of Statistics and Data Science, National University of Singapore, Singapore 119077, Singapore
Zihao Li: School of Mathematics and Physics, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China
Yuanyuan Fang: Metropolitan College, Boston University, Boston, MA 02215, USA
Jingchen Li: School of Software, Shandong University, Jinan 250101, China
Wenqi Sun: School of Computer and Artificial Intelligence, Shandong University of Finance and Economics, Jinan 250020, China
Bohan Zhang: College of Electronic Engineering, Faculty of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
Changzhi Li: IEIT SYSTEMS Co., Ltd., Jinan 250000, China
Xin Li: School of Software, Shandong University, Jinan 250101, China

Mathematics, 2025, vol. 13, issue 15, 1-16

Abstract: 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.

Keywords: knowledge graph; mixture of experts; multimodal knowledge graph (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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