Detecting Trends in Social Bookmarking Systems: A del.icio.us Endeavor

被引：8

作者：

Wetzker, Robert ^{[1
]}

Zimmermann, Carsten ^{[2
]}

Bauckhage, Christian ^{[3
,4
]}

机构：

[1] Tech Univ Berlin, Distributed Artificial Intelligence DAI Lab, Res Grp, D-1000 Berlin, Germany

[2] Univ San Diego, San Diego, CA 92110 USA

[3] Univ Bonn, D-5300 Bonn, Germany

[4] Fraunhofer IAIS, Multimedia Pattern Recognit Grp, Aachen, Germany

来源：

INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING | 2010年 / 6卷 / 01期

关键词：

Collaborative Tagging; Delicious; Folksonomies; Social Bookmarking; Spam Detection; Trend Detection; FOLKSONOMIES;

D O I：

10.4018/JDWM.2010090803

中图分类号：

TP31 [计算机软件];

学科分类号：

081202 ; 0835 ;

摘要：

The authors present and evaluate an approach to trend detection in social bookmarking systems using a probabilistic generative model in combination with smoothing techniques. Social bookmarking systems are gaining major interest among researchers in the areas of data mining and Web intelligence, since they provide a large amount of user-generated annotations and reflect the interest of millions of people. Based on a vast cot-pus of approximately 150 million bookmarks found at del.icio.us, the authors analyze bookmarking and tagging patterns and discuss evidence that social bookmarking systems are vulnerable to spamming. They present a method to limit the impact Of spam on a trend defector and provide conclusions as well as directions for future research.

引用

页码：38 / 57

页数：20

共 9 条

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