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在元路径诱导的图形上进行交叉视图对比表示学习,这些图形具有捆绑推的节点特征.

Peng Zhang1, Zhendong Niu2, Ru Ma3

  • 1organization=School of Computer Science and Technology, Beijing Institute of Technology, city=Beijing, country=China.

Neural networks : the official journal of the International Neural Network Society
|February 3, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的交叉视图对比表示学习 (CCRL) 方法,用于捆绑推. CCRL有效地建模复杂的用户捆绑关系,在建议相关项目方面超过现有方法.

关键词:
捆绑推建议是一套建议.相反的学习学习.图形表示学习学习学习图形表示.由元路径诱导的图形.推系统是推系统.

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科学领域:

  • 计算机科学 计算机科学
  • 人工智能的人工智能
  • 机器学习 机器学习

背景情况:

  • 捆绑推建议以整体的形式建议相关项目,而不是按项目进行的方法.
  • 对比式学习 (CL) 增强了从项目和捆绑视图的节点表示,以获得更好的建议.
  • 现有的CL方法不充分模拟用户-用户和捆绑-捆绑关系,并滥用图形结构来选择样本.

研究的目的:

  • 为了解决当前捆绑推方法的缺陷.
  • 提出一种新的方法,交叉视图对比表示学习 (CCRL),用于捆绑推.
  • 改进高阶关系的建模,完善对比学习中的样本选择.

主要方法:

  • 引入了元路径,以构建具有节点特征的元路径诱导图,从项目和捆绑视图建模用户-用户和捆绑-捆绑关系.
  • 在这些图表上执行图表表示学习,以获得用户和捆绑表示.
  • 开发了一种新型的对比损失,支持多个积极样本,用于交叉查看图的CL机制,以改进表示.
  • 使用联合优化目标训练模型.

主要成果:

  • 拟议的CCRL方法有效地同时模拟用户-用户和捆绑-捆绑关系.
  • 新的对比损失通过利用图形结构来改善正负样本的选择.
  • 对基准数据集的实验表明,CCRL在捆绑推中明显优于现有的基线方法.

结论:

  • 拟议的CCRL方法通过增强代表性学习,为捆绑推提供了一种优越的方法.
  • 显式建模高阶关系和完善对比学习策略,可以提高推的性能.
  • 这项工作为以图形为基础的推系统的未来研究提供了坚实的基础.