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基于高阶依赖网络的用户行为研究.

Liwei Qian1, Yajie Dou1, Chang Gong1

  • 1College of Systems Engineering, National University of Defense Technology, Changsha 410073, China.

Entropy (Basel, Switzerland)
|August 26, 2023
PubMed
概括
此摘要是机器生成的。

使用高阶依赖网络 (HONs) 分析日常行为可以改善需求挖掘. 这种方法提高了识别关键用户行为和理解社区结构的准确性,有利于个性化的建议和产品开发.

关键词:
行为序列分析 行为序列分析社区检测 社区检测高级依赖性网络 (HONs) 是一个高级依赖性网络.随机步行随机步行随机步行重要节点识别 重要节点识别

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科学领域:

  • 计算机科学 计算机科学
  • 数据挖掘 数据挖掘
  • 网络分析 网络分析

背景情况:

  • 物联网 (IoT) 数据可以分析日常生活行为,以确定用户的需求.
  • 当前的网络方法,如第一阶网络 (FON),忽视了行为序列中的关键高阶依赖关系.

研究的目的:

  • 为更准确的用户行为分析引入一个更高阶依赖网络 (HON) 模型.
  • 通过考虑复杂的行为关系来改进用户需求的挖掘.

主要方法:

  • 来自视频检测的行为序列被用来提取更高阶的依赖规则并构建一个HON.
  • 该HON被应用到RandomWalk算法,用于重要的节点识别和社区检测.

主要成果:

  • 与FON相比,HON显著提高了随机步行算法的准确性.
  • 加强了对重要节点的识别,并观察到节点可以属于多个社区.
  • 该研究表明,用户行为分析的性能有所改善.

结论:

  • 高级依赖网络为分析用户行为和挖矿需求提供了更准确的方法.
  • 调查结果支持增强个性化的建议,产品改进和增加商业利.
  • 这项研究强调了在对行为数据的网络分析中考虑复杂的依赖关系的重要性.