基于多尺度注意力和对比学习的图形神经网络推算法
Dongqi Pu1,2, Yaming Zhang3, Zhenghong Qian1,2
1School of Information Science and Technology, Yunnan Normal University, Kunming, 650500, China.
Scientific reports
|September 1, 2025
概括
这项研究引入了一个图形神经网络推系统 (GR-MC),可以提高稀疏数据的性能. 通过图形增强和对比学习,GR-MC提高了用户对项目表示的学习,提高了推的准确性.
科学领域:
- 计算机科学
- 人工智能
- 机器学习
背景情况:
- 推系统难以处理用户与项目之间的交互数据,这阻碍了对象的学习和整体性能.
- 现有的方法往往无法有效地建模更高阶的用户-项目关系或减轻图形结构中的偏差.
研究的目的:
- 提出一种基于图形神经网络的建议算法 (GR-MC),旨在应对稀缺数据带来的挑战.
- 提高用户和项目表示的质量,以提高建议的准确性和稳定性.
主要方法:
- 实施了使用以用户为中心的边缘脱落来减少度偏差的图形结构增强策略.
- 引入了多层次的注意机制,以改善嵌入式传播和更高层次关系的建模.
- 纳入对比学习作为自我监督的任务,以增强嵌入区分能力和模型的稳定性.
主要成果:
- 与现有方法相比,GR-MC在多个公共数据集中表现出更高的性能.
- 在极为稀疏的亚马逊书籍数据集中,Recall@20取得了显著的24.69%的改进.
- 验证了模型的有效性和稳定性,特别是在互动数据有限的环境中.
结论:
- 拟议的GR-MC算法有效地克服了稀少的用户-项目交互数据的局限性.
- 多层次的注意力和对比学习显著提高了表现学习和推表现.
- 对于在数据稀缺的情况下运行的推系统,GR-MC提供了一个强大的解决方案.
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