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Enhancing graph contrastive learning with knowledge graph embedding for recommendation

delete2025-12-01
delete0
PRE
AI
T
Tao Xie
X
Xiaofeng Wang *
T
Tianxiang Lv
S
Shuaiming Lai
X
X. R. Zheng
D
Daying Quan
Y
Yuanyuan Qi
X
Xiaofeng Huang *
DOI：10.1016/j.neucom.2025.132283delete
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Abstract

Abstract

En 中文
• A novel integration of KG embeddings with graph contrastive learning enhances the expressiveness of user-item interactions. • A low-rank approximation preserves global structural features, overcoming limitations of random perturbations in GCL. • A unified framework incorporating KG semantics into user-item graphs enables richer feature and improved interpretability.

Journal

Neurocomputing cover
Neurocomputing
IF:
6.5
Papers:
2.5W
Citations:
6.5W

Organization

No organization information available