图表神经网络为偏好的社会建议
Gang-Feng Ma1, Xu-Hua Yang1, Yue Tong1
1College of Computer Science and Technology, Zhejiang University of Technology, Hangzhou, Zhejiang, China.
PeerJ. Computer science
|June 22, 2023
概括
这项研究引入了一种使用图形神经网络 (GNN) 进行社会推的新方法. 拟议的方法通过解决社交网络数据和用户表示中的问题来提高推准确性,提高性能,特别是对于新用户来说.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 数据挖掘 数据挖掘
背景情况:
- 社交推系统利用社交网络的信息来增强用户的推.
- 现有的图形神经网络 (GNN) 方法在社交网络数据方面面临挑战,包括连接用户之间的偏好不一致以及用户表示中的信息冗余.
研究的目的:
- 为基于偏好的社会推提出一个新的图形神经网络 (GNN) 框架.
- 解决当前GNN应用在社会推中的局限性,特别是偏好错位和信息冗余.
主要方法:
- 引入了一个"朋友影响指标",以创建一个社会偏好网络,反映用户偏好相似性.
- 为了防止信息冗余,为社会偏好网络和用户项目交互网络使用了不同的GNN.
- 利用两个损失函数来维持连接关系,并区分正负样本.
主要成果:
- 拟议的优先社会建议 (PSR) 模型在推任务中表现出有效性.
- PSR是轻量级的,在解决冷启动建议问题上表现出特别强大的力量.
- 实验结果验证了该模型提高推性能的能力.
结论:
- 拟议的图形神经网络方法有效地整合了社会偏好信息,以改进建议.
- PSR提供了一种轻量级和高效的解决方案,特别有利于推系统中的冷启动场景.
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