[Purpose/significance] It is of great significance for the study of social network information ecology and information recommendation to accurately grasp the interest tendency of social network users and classify users into highly aggregated user groups.[Method/process] In this paper, by constructing the user attributes describe hierarchical model based on multi-dimensional, according to the model data requirements fetching user sample data from Sina microblog, quantify the secondorder variable based on the multi-dimensional property of the users' background information, users' blog information and user behavior information to construct user vector expression, comparing the classification results based on single dimension and the multi-dimensional, given different weights to attribute for weighted analysis, when achieve the optimal clustering results, based it do variance analysis to improve the model.[Result/conclusion] User clustering effect based on the multi-dimensional attribute weighting is significantly better than the user clustering effect based on the single-dimensional and under the condition of the multidimensional unweighted, and users microblog content dimension for improving the validity of user clustering effect is the largest.
Zhang Haitao
,
Tang Shiman
,
Wei Mingzhu
,
Li Zezhong
. Research on the Clustering of Microblog Users Based on Multi-dimensional Attribute Weighting Analysis[J]. Library and Information Service, 2018
, 62(24)
: 124
-133
.
DOI: 10.13266/j.issn.0252-3116.2018.24.016
[1] ALARCóN-DEL-AMO M C, LORENZO-ROMERO C, GóMEZ-BORJA M Á. Classifying and profiling social networking site users:a latent segmentation approach[J]. Cyberpsychology, behavior, and social networking,2011,14(9):547-553.
[2] 张琳,谢忠红.基于聚类的微博用户类型与影响力研究[J].情报科学,2016,34(8):57-61.
[3] HANNON J,BENNETT M,SMYTH B.Recommending Twitter users to follow using content and collaborative filtering approaches[C]//Proceedings of the 4th ACM conference on pecommender systems. New York:ACM,2010:199-206.
[4] BLEI D M, NG A Y, JORDAN M I. Latent Dirichlet allocation[J]. Journal of machine learning research, 2003,3(1):993-1022.
[5] HONG L J,DAVISION B. Empirical study of topic modeling in Twitter[C]//Proceedings of the first workshop on social media analytics.New York:ACM Press,2010:80-88.
[6] EFRON M. Information search and retrieval in microblogs[J].Journal of the American Society for Information Science and Technology,2011,62(6):996-1008.
[7] 徐志明,李栋,刘挺,等.微博用户的相似性度量及其应用[J].计算机学报,2014,37(1):207-218.
[8] 黄静.消费型虚拟社区的用户行为特征及其应用研究[J].图书情报工作,2011,55(3):97-100,51.
[9] 崔金栋,孙遥遥,王欣,等.基于Folksonmy和本体融合的微博信息推荐方法研究[J].情报科学,2015,33(10):27-31.
[10] 薛云霞. 微博用户属性识别方法研究[D].苏州:苏州大学,2015.
[11] 顾晓雪,章成志.标注内容与用户属性结合的标签聚类研究[J].现代图书情报技术,2015(10):30-39.
[12] 彭希羡,朱庆华,刘璇.微博客用户特征分析及分类研究——以"新浪微博"为例[J].情报科学,2015,33(1):69-75.
[13] 张国英,沙云,刘旭红,等.高维云模型及其在多属性评价中的应用[J].北京理工大学学报,2004(12):1065-1069.
[14] 李小慧. 基于Jaccard项目类别相似性的个性化推荐算法研究[D].长沙:中南大学,2010.
