双层图形对比协作过的协作过
Jiahao Wang1, Qingshuai Wang1, Kai Ma1
1School of Computer Sciences, Universiti Sains Malaysia, Penang, 11800, Malaysia.
Scientific reports
|July 9, 2025
概括
本研究引入了一种双层图对比协作过方法 (DL-GCL),以增强推系统. 通过结合节点和图表层次视图,DL-GCL有效地解决了数据稀疏性,并提高了性能.
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:
- 基于图形的协作过对于推系统来说是有效的,但与数据稀疏性作斗争.
- 图形协作过中的现有对比学习方法使用单视图设计,限制性能.
研究的目的:
- 提出一种新的双层图对比协作过方法 (DL-GCL).
- 通过整合图表和节点级视图来提高推系统的稳定性和减轻数据稀疏性.
主要方法:
- DL-GCL使用矩阵分解来构建节点级对比视图.
- 使用快速梯度标志方法 (FGSM) 来减轻噪声,生成图表级的对比视图.
- 多任务学习策略优化了局部-全球视图和贝叶斯个性化排名 (BPR) 的损失.
主要成果:
- 在四个数据集上的实验显示,推性能显著改善.
- 与最先进的图形对比模型相比,NDCG和RECALL的指标提高了高达24.5%.
结论:
- DL-GCL有效地提高了推系统的稳定性.
- 双层对比方法成功地减轻了数据稀疏性问题.
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