Community detection in social tagging systems based semantics of tags

Mengmeng Shen; Jun Wang; Xingyan Liu

doi:10.1145/3195106.3195156

Community detection in social tagging systems based semantics of tags

Mengmeng Shen
, Jun Wang
, Xingyan Liu

School of Economics and Management

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

1 Scopus citations

Abstract

Community detection is an important technique in social tagging systems (STSs), which is of great value to personalized recommendation, information retrieval and user behavior analysis. In this paper, in terms of the agglomerative hierarchical clustering (AHC), a community detection algorithm based on semantics of tags is proposed, named as I-ACST (an improved agglomerative clustering based on semantics of tags). First, we directly use t ag co-occurrence to measure the tag semantic relevance, and then tags are merged using I-ACST algorithm. Finally, the proposed algorithm is tested on real network, the experimental results show that I-ACST algorithm enhances the clustering performance than other clustering algorithms, and verifies the availability and effectiveness.

Original language	English
Title of host publication	Proceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018
Publisher	Association for Computing Machinery
Pages	69-73
Number of pages	5
ISBN (Electronic)	9781450363532
DOIs	https://doi.org/10.1145/3195106.3195156
State	Published - 26 Feb 2018
Event	10th International Conference on Machine Learning and Computing, ICMLC 2018 - Macau, China Duration: 26 Feb 2018 → 28 Feb 2018

Publication series

Name	ACM International Conference Proceeding Series

Conference

Conference	10th International Conference on Machine Learning and Computing, ICMLC 2018
Country/Territory	China
City	Macau
Period	26/02/18 → 28/02/18

Keywords

AHC
Community detection
Social tagging systems
Tag clustering

Access to Document

10.1145/3195106.3195156

Cite this

@inproceedings{06306f9776c347b0b41155b185f8d9c9,

title = "Community detection in social tagging systems based semantics of tags",

abstract = "Community detection is an important technique in social tagging systems (STSs), which is of great value to personalized recommendation, information retrieval and user behavior analysis. In this paper, in terms of the agglomerative hierarchical clustering (AHC), a community detection algorithm based on semantics of tags is proposed, named as I-ACST (an improved agglomerative clustering based on semantics of tags). First, we directly use t ag co-occurrence to measure the tag semantic relevance, and then tags are merged using I-ACST algorithm. Finally, the proposed algorithm is tested on real network, the experimental results show that I-ACST algorithm enhances the clustering performance than other clustering algorithms, and verifies the availability and effectiveness.",

keywords = "AHC, Community detection, Social tagging systems, Tag clustering",

author = "Mengmeng Shen and Jun Wang and Xingyan Liu",

note = "Publisher Copyright: {\textcopyright} 2018 Association for Computing Machinery.; 10th International Conference on Machine Learning and Computing, ICMLC 2018 ; Conference date: 26-02-2018 Through 28-02-2018",

year = "2018",

month = feb,

day = "26",

doi = "10.1145/3195106.3195156",

language = "英语",

series = "ACM International Conference Proceeding Series",

publisher = "Association for Computing Machinery ",

pages = "69--73",

booktitle = "Proceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018",

address = "美国",

}

Shen, M, Wang, J & Liu, X 2018, Community detection in social tagging systems based semantics of tags. in Proceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018. ACM International Conference Proceeding Series, Association for Computing Machinery , pp. 69-73, 10th International Conference on Machine Learning and Computing, ICMLC 2018, Macau, China, 26/02/18. https://doi.org/10.1145/3195106.3195156

Community detection in social tagging systems based semantics of tags. / Shen, Mengmeng; Wang, Jun; Liu, Xingyan.
Proceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018. Association for Computing Machinery , 2018. p. 69-73 (ACM International Conference Proceeding Series).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Community detection in social tagging systems based semantics of tags

AU - Shen, Mengmeng

AU - Wang, Jun

AU - Liu, Xingyan

PY - 2018/2/26

Y1 - 2018/2/26

N2 - Community detection is an important technique in social tagging systems (STSs), which is of great value to personalized recommendation, information retrieval and user behavior analysis. In this paper, in terms of the agglomerative hierarchical clustering (AHC), a community detection algorithm based on semantics of tags is proposed, named as I-ACST (an improved agglomerative clustering based on semantics of tags). First, we directly use t ag co-occurrence to measure the tag semantic relevance, and then tags are merged using I-ACST algorithm. Finally, the proposed algorithm is tested on real network, the experimental results show that I-ACST algorithm enhances the clustering performance than other clustering algorithms, and verifies the availability and effectiveness.

AB - Community detection is an important technique in social tagging systems (STSs), which is of great value to personalized recommendation, information retrieval and user behavior analysis. In this paper, in terms of the agglomerative hierarchical clustering (AHC), a community detection algorithm based on semantics of tags is proposed, named as I-ACST (an improved agglomerative clustering based on semantics of tags). First, we directly use t ag co-occurrence to measure the tag semantic relevance, and then tags are merged using I-ACST algorithm. Finally, the proposed algorithm is tested on real network, the experimental results show that I-ACST algorithm enhances the clustering performance than other clustering algorithms, and verifies the availability and effectiveness.

KW - AHC

KW - Community detection

KW - Social tagging systems

KW - Tag clustering

UR - https://www.scopus.com/pages/publications/85048351111

U2 - 10.1145/3195106.3195156

DO - 10.1145/3195106.3195156

M3 - 会议稿件

AN - SCOPUS:85048351111

T3 - ACM International Conference Proceeding Series

SP - 69

EP - 73

BT - Proceedingsof 2018 10th International Conference on Machine Learning and Computing, ICMLC 2018

PB - Association for Computing Machinery

T2 - 10th International Conference on Machine Learning and Computing, ICMLC 2018

Y2 - 26 February 2018 through 28 February 2018

ER -

Community detection in social tagging systems based semantics of tags

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Keywords

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