Paper Info
Title
Optimizing Search and Ranking in Folksonomy Systems by Exploiting Context Information
Abstract
Tagging systems enable users to annotate resources with freely chosen keywords. The evolving bunch of tag assignments is called folksonomy and there exist already some approaches that exploit folksonomies to improve resource retrieval. In this paper, we analyze and compare graph-based ranking algorithms: FolkRank and SocialPageRank. We enhance these algorithms by exploiting the context of tags, and evaluate the results on the GroupMe! dataset. In GroupMe!, users can organize and maintain arbitrary Web resources in self-defined groups. When users annotate resources in GroupMe!, this can be interpreted in context of a certain group. The grouping activity itself is easy for users to perform. However, it delivers valuable semantic information about resources and their context. We present GRank that uses the context information to improve and optimize the detection of relevant search results, and compare different strategies for ranking result lists in folksonomy systems.
Year
DOI
Venue
2009
10.1007/978-3-642-12436-5_9
Lecture Notes in Business Information Processing
Keywords
Field
DocType
Social media,Folksonomy systems,Search,Ranking,Optimization,FolkRank,GFolkRank,Social PageRank,GRank
Web resource,Data mining,Learning to rank,Graph,World Wide Web,Social media,Information retrieval,Ranking,Computer science,Exploit,Semantic information,Folksonomy
Conference
Volume
ISSN
Citations 
45
1865-1348
0
PageRank 
References 
Authors
0.34
11
3
Name
Order
Citations
PageRank
Fabian Abel1118762.22
Nicola Henze294690.27
Daniel Krause31287.84