用户推方法将层次图表注意力网络与多式联络知识图集成
Xiaofei Han1,2, Xin Dou2
1Business College, California State University, Long Beach, CA, United States.
Frontiers in neurorobotics
|July 3, 2025
概括
本研究引入了一种使用知识图和图形神经网络 (GNN) 的新型推模型,通过整合多式联网项目特征和解决冷启动问题来提高准确性.
科学领域:
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 图形神经网络 (GNN) 利用用户交互,但经常错过项目语义和多式联络功能,限制了推的多样性和准确性.
- 现有的方法在冷启动场景和整合各种数据类型的用户和项目表示方面扎.
研究的目的:
- 通过整合知识图,GNN和多式联络信息来增强用户和项目特征表示.
- 解决建议多样性,准确性和冷启动问题的局限性.
主要方法:
- 一个新的推模型,将层次图表注意力网络与多式联络知识图集成.
- 该模型包含协作知识图神经层,图像特征提取和文本特征提取.
主要成果:
- 拟议的模型在两个公共数据集上显著优于现有的推方法.
- 通过增强的用户和项目功能提取,证明了推性能的改进.
结论:
- 将知识图和多式联网功能与GNN集成,提供了一种强大的方法来增强推系统.
- 该模型有效地解决了传统GNN的局限性,提高了准确性和处理新用户/项目.
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