社交网络用户标签预测研究

随着社交网站的流行以及用户的大规模增加,社交网络用户行为分析已经成为社交网站进行网站维护、性能优化和系统升级的重要基础,也是网络知识挖掘和信息检索的重要研究领域。为了更好地理解社交网络用户添加个人标签的行为特征,该文基于大约263万个微博用户的真实数据,对用户标签的分布进行了研究和分析。我们主要考察了用户标签的宏观分布特征,以及用户标签与关注对象的标签分布之间的联系,发现微博用户给自己添加标签时,在开始阶段倾向于使用反映个性的标签,之后会出于从众心理而选用大众化标签。我们将研究发现运用到基于关注关系的标签预测算法中,结果证实相关分析对于社交网站的标签推荐等课题具有一定的参考意义。

User Behavior Analysis of Person Tags in SNS

With the popularity of social network sites (SNS) and the massive increase in SNS users, the behavior analysis of SNS users is of substantial importance in website maintenance, performance optimization and system upgrade. Its also a very important research area of network knowledge mining and information retrieval. For a better understanding of the user behaviors in adding tags for themselves in SNS, this paper analyses the distribution of user tags based on the data of about 2.63 million Weibo users. This paper investigates the macroscopic distribution characteristics of user tags, and the relation of tag distributions between a user and the people he follows. We reveal that when Weibo users add tags for themselves, they tend to use tags which can reflect their characteristics in the beginning, then, they tend to select popular tags out of a herd mentality. We applied research findings to a tag prediction algorithm based on following relationships, and the results prove that the correlation analysis provides certain reference significance to tag recommendation in social networks.