噪音增强的对比学习,注意知识意识的协作建议
Wanyi Gu1, Hua Xu2, Xiang Peng1
1Information and Navigation College, Air Force Engineering University, Shannxi, China.
Scientific reports
|October 2, 2025
概括
这项研究引入了一种新的噪音增强知识图注意力对比学习 (NA-KGACL) 方法来增强推系统. 通过通过噪声增强和多层次的对比框架来解决数据稀疏性,NA-KGACL提高了建议准确性和培训效率.
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:
- 知识图 (KG) 对推系统至关重要,其中图形卷积网络 (GCN) 和图形注意网络 (GAT) 主导着协作知识图 (CKG) 模型.
- 大规模的推系统面临长尾分布和数据稀疏性的挑战,导致实体嵌入分布不均.
- 对比学习 (CL) 通过学习一般表示来帮助减轻数据稀疏性,但传统的图形增强技术对于基于CL的建议来说是不理想的.
研究的目的:
- 提出一种新的方法,噪音增强知识图注意力对比学习 (NA-KGACL),以改进推系统.
- 在基于CL的建议中解决现有的图形增大技术的局限性.
- 为了提高处理长尾分布和数据稀疏性在大规模的基于图形的推系统.
主要方法:
- 开发了一个多层次的对比框架,将CL与Knowledge-GAT集成在一起.
- 使用投影头和混合批量规范化的精细节点表示.
- 引入了噪声增强算法,以取代无效的图形增强方法,以生成对比的学习视图.
主要成果:
- 拟议的NA-KGACL方法在三个大规模的现实世界数据集上展示了改进的学习表征.
- 实验结果显示,与现有方法相比,建议准确度增加.
- 该研究表明,使用NA-KGACL方法,培训流程更有效.
结论:
- 在基于图表的推系统中,NA-KGACL有效地解决了数据稀疏性和长尾分布问题.
- 噪音增强策略为产生对比观点提供了一个强大的替代方案.
- 该方法在推性能和培训效率方面都提供了显著的改进.
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