Graph based techniques for tag cloud generation.

Martin Leginus; Peter Dolog; Ricardo Gomes Lage

doi:10.1145/2481492.2481508

Graph based techniques for tag cloud generation.

Martin Leginus, Peter Dolog, Ricardo Gomes Lage

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

6 Citations (Scopus)

Abstract

Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.

Original language	English
Title of host publication	Proceedings of the 24th ACM Conference on Hypertext and Social Media
Number of pages	10
Place of Publication	New York, NY, USA
Publisher	Association for Computing Machinery
Publication date	May 2013
Pages	148-157
ISBN (Print)	978-1-4503-1967-6
DOIs	https://doi.org/10.1145/2481492.2481508
Publication status	Published - May 2013
Event	24th ACM Conference on Hypertext and Social Media - Paris, France Duration: 1 May 2013 → 3 May 2013 Conference number: 24

Conference

Conference	24th ACM Conference on Hypertext and Social Media
Number	24
Country/Territory	France
City	Paris
Period	01/05/2013 → 03/05/2013

Keywords

Tag cloud
Graph Theory

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10.1145/2481492.2481508

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@inproceedings{cb23cc3782f64395900b8aacdcd48a59,

title = "Graph based techniques for tag cloud generation.",

abstract = "Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.",

keywords = "Tag cloud, Graph Theory",

author = "Martin Leginus and Peter Dolog and Lage, {Ricardo Gomes}",

year = "2013",

month = may,

doi = "10.1145/2481492.2481508",

language = "English",

isbn = "978-1-4503-1967-6",

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booktitle = "Proceedings of the 24th ACM Conference on Hypertext and Social Media",

publisher = "Association for Computing Machinery",

address = "United States",

note = "24th ACM Conference on Hypertext and Social Media, HT'13 ; Conference date: 01-05-2013 Through 03-05-2013",

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Graph based techniques for tag cloud generation. / Leginus, Martin; Dolog, Peter; Lage, Ricardo Gomes.
Proceedings of the 24th ACM Conference on Hypertext and Social Media. New York, NY, USA: Association for Computing Machinery, 2013. p. 148-157.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - Graph based techniques for tag cloud generation.

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AU - Dolog, Peter

AU - Lage, Ricardo Gomes

N1 - Conference code: 24

PY - 2013/5

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N2 - Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.

AB - Tag cloud is one of the navigation aids for exploring documents. Tag cloud also link documents through the user defined terms. We explore various graph based techniques to improve the tag cloud generation. Moreover, we introduce relevance measures based on underlying data such as ratings or citation counts for improved measurement of relevance of tag clouds. We show, that on the given data sets, our approach outperforms the state of the art baseline methods with respect to such relevance by 41 % on Movielens dataset and by 11 % on Bibsonomy data set.

KW - Tag cloud

KW - Graph Theory

U2 - 10.1145/2481492.2481508

DO - 10.1145/2481492.2481508

M3 - Article in proceeding

SN - 978-1-4503-1967-6

SP - 148

EP - 157

BT - Proceedings of the 24th ACM Conference on Hypertext and Social Media

PB - Association for Computing Machinery

CY - New York, NY, USA

T2 - 24th ACM Conference on Hypertext and Social Media

Y2 - 1 May 2013 through 3 May 2013

ER -

Graph based techniques for tag cloud generation.

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Keywords

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