...
  • 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Knowledge and Data Engineering, IEEE Transactions on >Structured Learning from Heterogeneous Behavior for Social Identity Linkage
【24h】

Structured Learning from Heterogeneous Behavior for Social Identity Linkage

机译：从异构行为的结构化学习中进行社会身份关联

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Social identity linkage across different social media platforms is of critical importance to business intelligence by gaining from social data a deeper understanding and more accurate profiling of users. In this paper, we propose a solution framework, , which consists of three key steps: (I) we model heterogeneous behavior by long-term topical distribution analysis and multi-resolution temporal behavior matching against high noise and information missing, and the behavior similarity are described by multi-dimensional similarity vector for each user pair; (II) we build structure consistency models to maximize the structure and behavior consistency on users’ core social structure across different platforms, thus the task of identity linkage can be performed on groups of users, which is beyond the individual level linkage in previous study; and (III) we propose a normalized-margin-based linkage function formulation, and learn the linkage function by multi-objective optimization where both supervised pair-wise linkage function learning and structure consistency maximization are conducted towards a unified optimal solution. The model is able to deal with drastic information missing, and avoid the curse-of-dimensionality in handling high dimensional sparse representation. Extensive experiments on 10 million users across seven popular social networks platforms demonstrate that correctly identifies real user linkage across different platforms from massive noisy user behavior data records, and outperforms existing state-of-the-art approaches by at least 20 percent under differen- settings, and four times better in most settings.
机译：通过从社交数据中获得对用户的更深入的了解和更准确的配置，跨不同社交媒体平台的社交身份链接对于商业智能至关重要。在本文中，我们提出了一个解决方案框架，该框架包括三个关键步骤：（I）通过长期主题分布分析和针对高噪声和信息丢失以及行为相似性的多分辨率时间行为匹配，对异构行为进行建模通过每个用户对的多维相似度向量进行描述； （II）我们建立结构一致性模型，以最大程度地跨平台跨用户核心社会结构的结构和行为一致性，从而可以对用户组执行身份链接的任务，这超出了先前研究中的个人级别链接； （III）提出了基于归一化余量的链接函数公式，并通过多目标优化学习链接函数，其中有监督的成对链接函数学习和结构一致性最大化都朝着统一的最优解进行。该模型能够处理严重的信息丢失，并避免在处理高维稀疏表示时出现维数诅咒。在七个流行的社交网络平台上对1000万用户进行的广泛实验表明，可以从大量嘈杂的用户行为数据记录中正确识别出跨不同平台的真实用户链接，并且在不同环境下的性能比现有的最新方法高出至少20％ ，在大多数设置下要好四倍。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号