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中文摘要：
微博用户的兴趣分析和模型表示是用户关系分析的基础,而用户关系分析又构成了微博社会网络的生成和分析的基础.该文主要讨论微博的用户关系分析技术.作者将微博社会网络视为一个加权无向图,节点表示用户,边表示用户之间的关系,边的权值表示用户之间的关系强度.该文将用户关系强度定义为用户之间的相似度,分别给出了基于各种用户属性信息（背景信息、微博文本、社交信息）的用户相似度计算方法,并通过实验系统性对比了上述方法的优劣.实验结果显示：基于社交信息的用户相似度在用户关系分析方面取得了最好的效果.为了进一步验证上述用户相似度的实际性能,该文将它们应用于用户推荐的相关实验,基于社交信息的用户相似度又取得了最好的推荐效果.最后,该文应用基于社交信息的用户相似度生成了微博的社会网络（称作用户相似性网络）,在该社会网络上进行了团体挖掘的实验,实验结果显示了该相似度在团体挖掘上的有效性.
英文摘要：
Analyzing user interest and building user profile is very important for microblog＇s user relationship analysis,which is the fundamental work for social network formation and analysis.This paper mainly discusses approaches of microblog＇s user relationship analysis.We view microblog＇s social network as a weighted undirected graph,where users are treated as nodes linked by edges,and the weights of edges mean the relationship strengths between users.This paper defines user relationship strength as user similarity,and proposes several user similarity estimation approaches by the use of various attribute information of users such as background information,tweets and social information respectively and systematically investigated them by experiments,the experimental results showed that social information-based user similarity achieved the best performance.In addition,we tested them in user recommending experiments,and social information-based user similarity also got the best recommending results.Finally we applied social information-based user similarity to generate microblog＇s social network,called as user similarity network,on which we conducted community mining experiments,the experimental results showed our approach is of remarkable performance.
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