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一个使用光图卷积网络和个性化知识意识注意力子网络的新推系统.
Rasoul Hassanzadeh1, Vahid Majidnezhad2, Bahman Arasteh3,4,5
1Department of Computer Engineering, Shabestar Branch, Islamic Azad University, Shabestar, Iran.
Scientific reports
|May 5, 2025
概括
本研究介绍了LGKAT,这是一种新的推系统,通过整合用户项目和知识图来增强个性化知识意识的建议. 通过有效地建模复杂的关系和用户偏好,LGKAT提高了推质量.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 图形神经网络 (GNN) 在推系统 (RS) 中越来越多地用于特征提取和关系建模.
- 在捕获细粒度知识图 (KG) 语义和有效建模用户-项目交互方面,GNN面临着挑战.
- 个性化知识意识建议提供了一个有希望的方法来解决这些局限性.
研究的目的:
- 提出一个新的推系统,LGKAT,它结合了用户项目图表和知识图表,以提高推准确性.
- 通过利用KG的丰富语义信息来增强用户-项目交互的建模.
- 解决现有的基于GNN的推系统在捕捉微妙关系方面的局限性.
主要方法:
- 开发了LGKAT,这是一个结合用户项目和知识图的推系统.
- 员工光图卷积网络 (LightGCN) 用于有效管理用户和项目嵌入.
- 引入了一个注意力子网络,将KG语义编码到个性化的项目嵌入中.
主要成果:
- 在四个基准数据集上进行了广泛的实验.
- 在F1_score和回忆方面,LGKAT显著优于最先进的方法.
- 集成LightGCN和注意力机制有效地提高了推质量.
结论:
- 通过整合知识图,LGKAT有效地解决了当前推者系统的局限性.
- 提出的方法在个性化知识意识的推任务中实现了卓越的性能.
- 这项研究突出了将GNN与知识图相结合的潜力,以加强推系统.


