跨社交媒体网络大数据下的用户建模

doi:10.11959/j.issn.2096-0271.2016052

大数据 ›› 2016, Vol. 2 ›› Issue (5): 32-42.doi: 10.11959/j.issn.2096-0271.2016052

跨社交媒体网络大数据下的用户建模

项连城^1,²,桑基韬^1,²,徐常胜^1,²

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

出版日期:2016-09-20 发布日期:2018-02-08
基金资助:
国家自然科学基金资助项目;国家自然科学基金资助项目;国家自然科学基金资助项目

Cross-OSN user modeling in big data

Liancheng XIANG^1,²,Jitao SANG^1,²,Changsheng XU^1,²

¹ Institute of Automation,Chinese Academy of Sciences,Beijing 100190,China
² University of Chinese Academy of Sciences,Beijing 100049,China

Online:2016-09-20 Published:2018-02-08
Supported by:
The National Natural Science Foundation of China;The National Natural Science Foundation of China;The National Natural Science Foundation of China

摘要/Abstract

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。

关键词: 跨社交媒体网络, 用户建模, 人口属性, 兴趣属性

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.

Key words: cross-OSN, user modeling, demographic attribute, interest attribute

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): 32-42.

Liancheng XIANG, Jitao SANG, Changsheng XU. Cross-OSN user modeling in big data[J]. Big Data Research, 2016, 2(5): 32-42.

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	性别	年龄	婚姻状况	职业
SVM_Google+	0.747 37	0.693 33	0.583 01	0.391 04
SVM_Twitter	0.760 03	0.680 00	0.584 92	0.372 58
PME_Google+	0.742 45	0.654 04	0.580 92	0.381 61
PME_Twitter	0.755 21	0.640 84	0.582 36	0.366 81
CPME	0.779 90	0.716 04	0.600 84	0.420 93

	性别	年龄	婚姻状况	职业
Google+	0.751 11	0.706 36	0.592 82	0.393 50
Twitter	0.771 32	0.708 94	0.590 27	0.389 14
跨网络	0.779 90	0.716 04	0.600 84	0.420 93

跨社交媒体网络大数据下的用户建模

Cross-OSN user modeling in big data

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