福建电脑2026，Vol.42Issue(3)：11-16,6.DOI:10.16707/j.cnki.fjpc.2026.03.003
电商推荐中知识图谱嵌入模型的比较研究
Comparative Analysis of Knowledge Graph Embedding Models in E-Commerce Recommendation
张恒1
作者信息
- 1. 北京工奇科技有限公司研发部 北京 100029
- 折叠
摘要
Abstract
This study evaluated the performance of three typical knowledge graph embedding models,TransE,Rotate,and ComplEx,in e-commerce recommendation scenarios.Building a unified experimental platform based on the OpenBG500 dataset,conducting link prediction evaluation and segmenting relationship types for analysis.The experimental results showed that ComplEx performed the best overall(MRR 0.369,Hits@1 0.326)has outstanding advantages in modeling symmetric and asymmetric relationships;TransE training is efficient but has limited fine-grained modeling capabilities;Rotate is slightly better than ComplEx in a one to many relationship.This indicates that ComplEx is more suitable for multi relationship representation learning in e-commerce knowledge graphs,and can provide reference for cold start mitigation and long tail product support in recommendation systems.关键词
知识图谱/推荐系统/知识图谱嵌入Key words
Knowledge Graph/Recommendation System/Knowledge Graph Embedding分类
信息技术与安全科学引用本文复制引用
张恒..电商推荐中知识图谱嵌入模型的比较研究[J].福建电脑,2026,42(3):11-16,6.
