跨社交媒体网络大数据下的用户建模

项连城; 桑基韬; 徐常胜

doi:10.11959/j.issn.2096-0271.2016052

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跨社交媒体网络大数据下的用户建模

研究 | 更新时间：2024-06-03

- 跨社交媒体网络大数据下的用户建模
- Cross-OSN user modeling in big data
- 大数据 2016年2卷第5期页码：2016052
- 作者机构：
  
  1. 中国科学院自动化研究所，北京 100190
  2. 中国科学院大学，北京 100049
- 作者简介：
  
  [ "项连城（1992-），女，中国科学院自动化研究所硕士生，主要研究方向为社交多媒体分析与挖掘。" ]
  [ "桑基韬（1985-），男，博士，中国科学院自动化研究所副研究员，主要研究方向为社会媒体分析、多媒体检索、数据挖掘。" ]
  [ "徐常胜（1969-），男，博士，中国科学院自动化研究所研究员，中国科学院大学博士生导师，主要研究方向为多媒体分析/索引/检索、模式识别、计算机视觉。" ]
- 基金信息：
  
  国家自然科学基金资助项目;The National Natural Science Foundation of China(61432019);国家自然科学基金资助项目;The National Natural Science Foundation of China(61225009);国家自然科学基金资助项目;The National Natural Science Foundation of China(61303176)
- DOI：10.11959/j.issn.2096-0271.2016052
  中图分类号： TP37
- 网络首发：2016-09，
  
  纸质出版：2016-09-20
- 稿件说明：
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项连城, 桑基韬, 徐常胜. 跨社交媒体网络大数据下的用户建模[J]. 大数据, 2016,2(5):2016052.

Liancheng XIANG, Jitao SANG, Changsheng XU. Cross-OSN user modeling in big data[J]. Big data research, 2016, 2(5): 2016052.
项连城, 桑基韬, 徐常胜. 跨社交媒体网络大数据下的用户建模[J]. 大数据, 2016,2(5):2016052. DOI： 10.11959/j.issn.2096-0271.2016052.

Liancheng XIANG, Jitao SANG, Changsheng XU. Cross-OSN user modeling in big data[J]. Big data research, 2016, 2(5): 2016052. DOI： 10.11959/j.issn.2096-0271.2016052.

摘要

社交媒体大数据中的多源性体现在不同社交媒体网络产生的内容上，从多源的角度分析跨社交媒体网络可以将独立数据的价值通过整合其他来源和模态的数据充分挖掘和释放出来，提高大数据的利用效率。跨社交媒体网络的用户建模是分析和应用多源社交媒体大数据的重要体现。跨社交媒体网络中的多源数据共享独立用户空间，提出以用户为桥梁对多源数据进行关联挖掘，将挖掘得到的关联模式分别应用于跨社交媒体网络的用户人口属性建模和兴趣建模中，并应用到社交媒体应用的个性化服务中。

Abstract

Social media variety mainly concerns with the contents created and consumed in different online social network (OSN).Analyzing cross-OSN from the perspective of “variety” is beneficial to exerting the potential of big data

by integrally analyzing and exploiting the multi-sourced and multi-modal data.The problem of exploiting the cross-OSN data for comprehensive user modeling

which is fundamental in the context of multi-sourced social media big data was addressed.Inspired by the fact that the cross-OSN data shares unique user space

take the users as a bridge for associations mining between OSN was proposed.The discovered association patterns were then utilized in cross-OSN user demographic attribute inference and interest modeling in cross-OSN respectively

which can be further applied to personalized social media services.

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references

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