[1]李梦洁,邵曦.基于文本属性的微博用户相似度研究[J].计算机技术与发展,2018,28(05):17-22.[doi:10.3969/j.issn.1673-629X.2018.05.005]
　LI Meng-jie,SHAO Xi. Research on Micro－blog User Similarity Based on Text Similarity[J].,2018,28(05):17-22.[doi:10.3969/j.issn.1673-629X.2018.05.005]
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基于文本属性的微博用户相似度研究()

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
28
期数:
2018年05期
页码:
17-22
栏目:
智能、算法、系统工程
出版日期:
2018-05-10

文章信息/Info

Title:
 Research on Micro－blog User Similarity Based on Text Similarity
文章编号:
1673-629X(2018)05-0017-06
作者:
李梦洁; 邵曦
南京邮电大学 通信与信息工程学院，江苏 南京 210003
Author(s):
LI Meng-jie; SHAO Xi
School of Communication and Information Engineering,Nanjing University of Posts and Telecommunications,Nanjing 210003,China
关键词:
微博; 社交网络; 用户相似度; 文本相似度; 余弦相似度; 层次分析法
Keywords:
 ; Key words:Micro－blog; social network; user similarity; text similarity; cosine similarity; analytic hierarchy process
分类号:
TP301
DOI:
10.3969/j.issn.1673-629X.2018.05.005
文献标志码:
A
摘要:
传统的相似度计算方法忽略了用户主观输出的微博文本信息，而这正是体现用户兴趣点的重要元素，只有结合了用户自身信息及其在社交平台上的互动内容，才能相对全面描述一个用户，由此提出基于文本属性的相似用户计算方法。把相似度主要划分为背景相似、兴趣相似两个角度，其中兴趣相似度主要由文本相似度决定，分词并采用 TF－IDF 变换后进行余弦相似度计算;再结合用户所在地、使用设备、发微博的时间、转发关系以及好友关系来描述用户相似度;并利用层次分析法分配各个属性的权重，构建相似度计算模型。通过实验系统性对比了基于文本属性的计算方法与改进前的算法:改进算法得出的用户相似度的 F 1 度量值提高了 34．3%，证明了该算法的优越性。
Abstract:
Traditional similarity calculation method ignores the subjective information of the users,which is an important element that reflects user’s interest point.In order to fully describe the user’s information,the user’s background information and their interactive content on the social platform should be considered．Therefore,we present a calculating method of Micro－blog user similarity,which is bound up with text similarity.The user similarity is mainly divided by the background similarity and interest similarity which is mainly determined by the text similarity.The cosine similarity should be calculated after the word segmentation and TF－IDF.User similarity is also described by user’s location,the device they use,the time they send Weibo,the text they re－post and the relationship between them.Finally,the method uses AHP to determine the weight of each attribute and build an integrated similarity calculation model.Through the experiment,systematically compared with the calculating method of user similarity combined with text similarity and the one before improving,the results show that the former increase the F 1 metric by 34.3%,which shows its superiority.

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更新日期/Last Update: 2018-06-26