期刊文献+

博客资源数据挖掘研究现状 被引量:2

Research on Blog Data-Mining
下载PDF
导出
摘要 目前中国有3.73亿网民拥有博客,博客网站上已经存在海量的信息。对这些博客资源进行挖掘,可以获得有价值的信息。博客资源挖掘是Web数据挖掘的一种具体应用。探讨了国内外学者对博客资源进行数据挖掘的已有成果、各种方法与技术,涉及到博客网页的识别、博客传播特征、语义博客系统、博客之间的链接与交互、博客作者信息挖掘、博客主题挖掘、博客分类与聚类算法等。热点话题挖掘是博客数据挖掘的一种具体形式,也介绍了博客热点话题挖掘的方法与技术。 Currently,there are 373 million Internet users have a blog in China.There are vast amounts of information on the blog sites.Valuable information can be got through blog data-mining.The blog data-mining is a specific application of web data-mining.The achievements about blog data-mining,at home and abroad,have been discussed,including the identification of the blog page,blog propagation characteristics,semantic blog system,linking and interaction between blog pages,blog writer’s information mining,blog theme mining,blog classification and clustering algorithm.Hot topic mining is a specific form of blog data mining,the methods and techniques of it have been introduced too.
出处 《电脑知识与技术》 2013年第4X期2771-2773,共3页 Computer Knowledge and Technology
基金 安徽省教育厅人文社会科学研究项目"面向博客的专业知识热点话题挖掘研究"(编号:SK2013B443)
关键词 博客 BLOG WEB 数据挖掘 算法 热点话题挖掘 现状 blog web data-mining algorithm hot topic mining present situation
  • 相关文献

参考文献12

  • 1张程,陈自郁,古平,杨瑞龙.基于DOM树结构的Blog网页自动识别[J].计算机应用研究,2008,25(5):1489-1491. 被引量:8
  • 2杨宇航,赵铁军,郑德权,于浩.基于链接分析的重要Blog信息源发现[J].中文信息学报,2007,21(5):68-72. 被引量:6
  • 3胡方涛.Blog信息采集及热点话题支持度计算的系统实现[D].华南理工大学.2012
  • 4季文韬.基于数据挖掘的博客球聚类研究[D].西南石油大学.2011
  • 5杨宇航,赵铁军,郑德权,于浩.基于链接分析的重要Blog信息源发现[A].内容计算的研究与应用前沿——第九届全国计算语言学学术会议论文集.2007
  • 6时达明,林鸿飞.基于内容相关度和语义分析的Blog热点话题发现[A].内容计算的研究与应用前沿——第九届全国计算语言学学术会议论文集.2007
  • 7T. Nanno,T. Fujiki,Y. Suzuki,M. Okumura.Automatically Collection, Monitoring, and Mining Japanese Weblogs[].WWW.2004
  • 8Kolari P,Finin T,Joshi A.SVMs for the blogosphere:Blog identification and splog detection[].Procof the AAAI Spring Symp on Computational Approaches to Analyzing Weblogs.2006
  • 9I. Ohmukai,H. Takeda,K. Numa.Personal Knowledge Publishing Suite with Weblog[].Workshop on the Weblogging Ecosystem: Aggregation Analysis and Dynamics.2004
  • 10Adar E,Zhang L,Adamic LA,Lukose RM.Implicit structure and the dynamics of blogspace[].Procof the Workshop on the Weblogging Ecosystemtheth Int’l World Wide Web Conf.2004

二级参考文献21

  • 1王娜.博客搜索引擎与传统搜索引擎的比较研究[J].图书情报工作,2006,50(7):54-57. 被引量:11
  • 2Technorati.http://www.technorati.com[EB/OL].February 2007.
  • 3ComScore Networks,Inc.Behaviors of the blogosphere:understanding the scale,composition and activities of weblog audiences[EB/OL].http://www.comscore.com/blogreport /comScoreBlogReport.pdf.November 2005.
  • 4B.L.Tseng,Junichi Tatemura,Yi Wu.Tomographic clustering to visualize blog communities as mountain views[A].The 14th International World Wide Web Conference[C].Chiba:Japan,May 2005.
  • 5E.Adar,L.Zhang,L.Adamic,et al.Implicit structure and the dynamics of blogspace[A].The 13th International World Wide Web Conference[C].New York,USA:May 2004.
  • 6Ko Fujimura,Takafumi Inoue,Massayuki Sugisaki.The eigenrumor algorithm for ranking blogs[A].The 14th International World Wide Web Conference[C].Chiba,Japan:May 2005.
  • 7Shinsuke Nakajima,Junichi Tatemura,Yoichiro Hino.Discovering important bloggers based on analyzing blog threads[A].The 14th International World Wide Web Conference[C].Chibal,Japan:May 2005.
  • 8S.Brin,L.Page.The anatomy of a large-scale hypertextual web search engine[A].The 7th International World Wide Web Conference[C].Brisbane,Australia:April 1998.107-117.
  • 9J.Kleinberg.Authoritative sources in a hyperlinked environment[A].In:Proceedings of the 9th ACM-SIAM Symposium on Discrete Algorithms[C].New Orleans,America:January 1997.668-677.
  • 10S.Chakrabarti,B.Dom,D.Gibson,et al.Automatic resource compilation by analyzing hyperlink structure and associated text[A].The 7th International World Wide Web Conference[C].Brisbane,Australia:April 1998.65-74.

共引文献12

同被引文献34

  • 1中国互联网络信息中心.中国互联网络发展状况统计报告[EB/OL].http://www.cnnic.net.cn,2004—07—21/2004—08—09.
  • 2周涛.个性化推荐的十大挑战[J].中国计算机学会通讯,2012,8(7):48-61.
  • 3Kantor P B, Ricci F, Rokach L, et al. Recommender Systems Handbook[M]. 2011: Springer.
  • 4Bobadilla J, Ortega F, Hernando A, et al. Recommender Systems Survey[J]. Knowledge-Based Systems, 2013.
  • 5Heidemann J, Klier M,Probst F. Online Social Networks: A Survey of a Global Phenomenon[J]. Computer Networks, 2012, 56 (18): 3866-3878.
  • 6Zhou X, Xu Y, Li Y, et al. The State-of-the-Art in Personalized Recommender Systems for Social Networking[J]. Artificial Intelligence Review, 2012, 37(2):119-132.
  • 7Hoffman T. Online Reputation Management is Hot-But Is It Ethical[J]. Computerworld, February, 2008:1-4.
  • 8O'Leary D E. Blog Mining-Review and Extensions:"From Each According to His Opinion"[J]. Decision Support Systems, 2011, 51 (4):821-830.
  • 9Heymann P, Koutrika G,Garcia-Molina H. Can Social Bookmarking Improve Web Search? [C]. Proceedings of the International Con ference on Web Search and Web Data Mining. ACM. 2008:195-206.
  • 10Tso-Sutter K H, Marinho L B,Schmidt-Thieme L. Tag-Aware Recommender Systems by Fusion of Collaborative Filtering Algorithms [C]. Proceedings of the 2008 ACM Symposium on Applied Computing. ACM. 2008:1995-1999.

引证文献2

二级引证文献4

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部