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Knowledge-Aware Intent Subgraph Learning for Recommendation

delete2026-01-01
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PRE
AI
简萌 封面图
简萌 (Meng Jian) *
周
周魏 (Wei Zhou)
DOI：10.1007/978-981-95-3398-5_20delete
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摘要

摘要

En 中文
知识图谱（KG）增强推荐通过利用用户行为中的复杂语义线索缓解冷启动问题。然而，现有方法侧重于将这些辅助知识引入用户-项目交互空间，却忽略了不同来源之间的差异。本研究提出了一种知识感知意图子图学习（KISL）方法，利用KG挖掘用户意图以促进个性化推荐中的细粒度兴趣学习。我们在知识图谱上将每个意图表示建模为KG关系的注意力组合。在意图的指导下，KISL设计了一种维度解耦，将交互图划分为多个增强的意图感知子图。在子图消息传播过程中学习细粒度个性化嵌入，以预测用户的交互。在两个公开数据集上的大量实验证明了KISL通过知识感知意图建模优于基线方法的有效性。
Keyword:
Recommender system
knowledge graph
intent subgraph
interest disentangling

期刊

I
IMAGE AND GRAPHICS, ICIG 2025, PT I
IF:
0
论文数:
38
被引数:
0

机构

B
beijing university of technology
学者数:
6.0K
论文数: 2.0K
被引数: 0
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