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Temporal knowledge graph recommendation with sequence-aware and path reasoning

delete2025-10-03
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PRE
AI
Y
Yuanming Zhang *
Z
Ziyou He
Y
Yongbiao Lou
H
Haixia Long
高飞 cover
高飞 (Fei Gao)
DOI：10.1016/j.datak.2025.102522delete
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Abstract

Abstract

En 中文
• This paper presents a TKG that integrates interaction times, interaction relationships, user attributes, and item attributes, comprehensively capturing both the temporal and structural characteristics of user-item interactions, and providing a more complete data model for recommendation. • This paper proposes a novel TKGRec model that integrates sequence-aware and path reasoning. The sequence-aware module employs a dual-attention mechanism to extract temporal variation features of interactions, while the path reasoning module utilizes reinforcement learning to extract relational features of interaction paths, enabling a comprehensive representation of user preferences.

Journal

D
Data and Knowledge Engineering
IF:
2.6
Papers:
116
Citations:
1.7K

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