Title
A vector space approach to tag cloud similarity ranking
Abstract
One of the most exciting recent developments in web science is social tagging that enables users to easily annotate web content using free form keywords. Well known examples include Delicious, Flickr, and YouTube which respectively allow users to tag web pages, images, and videos. A tag cloud represents an aggregation of tags to characterize some entity of interest, and it has many potential applications particularly in the context of multimedia information retrieval and recommendation. In this paper, we present a novel method that computes the similarity between tag clouds through effectively incorporating tag similarity information. The considered problem has several unique characteristics mainly due to the informal nature of tag descriptions as well as the frequent tag updates, making it difficult to apply existing approaches in the information retrieval literature. Experimental results on Delicious data show that the proposed scheme can effectively utilize the tag similarity to improve the performance of tag cloud similarity ranking.
Year
DOI
Venue
2010
10.1016/j.ipl.2010.03.014
Inf. Process. Lett.
Keywords
Field
DocType
tag description,tag cloud,web page,information retrieval literature,tag cloud similarity ranking,annotate web content,vector space approach,multimedia information retrieval,tag similarity information,frequent tag updates,tag similarity,ranking,vector space,vector space model,web pages,information retrieval
Data mining,Similitude,Web page,Information retrieval,Similarity measure,Computer science,Multimedia information retrieval,noindex,Tag cloud,Vector space model,Web content
Journal
Volume
Issue
ISSN
110
12-13
0020-0190
Citations 
PageRank 
References 
1
0.37
10
Authors
3
Name
Order
Citations
PageRank
Jonghun Park149137.86
Byung-Cheon Choi216117.84
Kwanho Kim336137.49