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poster

Exploit the tripartite network of social tagging for web clustering

Published: 02 November 2009 Publication History

Abstract

In this poster, we investigate how to enhance web clustering by leveraging the tripartite network of social tagging systems. We propose a clustering method, called "Tripartite Clustering", which cluster the three types of nodes (resources, users and tags) simultaneously based on the links in the social tagging network. The proposed method is experimented on a real-world social tagging dataset sampled from del.icio.us. We also compare the proposed clustering approach with K-means. All the clustering results are evaluated against a human-maintained web directory. The experimental results show that Tripartite Clustering significantly outperforms the content-based K-means approach and achieves performance close to that of social annotation-based K-means whereas generating much more useful information.

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cover image ACM Conferences
CIKM '09: Proceedings of the 18th ACM conference on Information and knowledge management
November 2009
2162 pages
ISBN:9781605585123
DOI:10.1145/1645953
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 02 November 2009

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Author Tags

  1. folksonomy
  2. social tagging
  3. tag clustering
  4. tripartite network

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Cited By

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  • (2020)Making AppsACM Transactions on Computing Education10.1145/342571020:4(1-23)Online publication date: 12-Nov-2020
  • (2020)Interactive Stitch SamplerACM Transactions on Computing Education10.1145/341829920:4(1-29)Online publication date: 4-Oct-2020
  • (2020)Effect of Implementing Subgoals in Code.org's Intro to Programming Unit in Computer Science PrinciplesACM Transactions on Computing Education10.1145/341559420:4(1-24)Online publication date: 4-Oct-2020
  • (2020)Multimodal Coordination Measures to Understand Users and TasksACM Transactions on Computer-Human Interaction10.1145/341236527:6(1-26)Online publication date: 8-Nov-2020
  • (2020)Smell PittsburghACM Transactions on Interactive Intelligent Systems10.1145/336939710:4(1-49)Online publication date: 8-Nov-2020
  • (2018)The ecology of movement and behaviour: a saturated tripartite network for describing animal contactsProceedings of the Royal Society B: Biological Sciences10.1098/rspb.2018.0670285:1887(20180670)Online publication date: 19-Sep-2018
  • (2017)Leveraging Behavioral Factorization and Prior Knowledge for Community Discovery and ProfilingProceedings of the Tenth ACM International Conference on Web Search and Data Mining10.1145/3018661.3018693(71-79)Online publication date: 2-Feb-2017
  • (2017)Folksonomy-Based Internet Object Profiling and Relation ExtractingGLOBECOM 2017 - 2017 IEEE Global Communications Conference10.1109/GLOCOM.2017.8255084(1-6)Online publication date: Dec-2017
  • (2016)Co-clustering signed 3-partite graphsProceedings of the 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.5555/3192424.3192602(945-948)Online publication date: 18-Aug-2016
  • (2016)Co-clustering signed 3-partite graphs2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM)10.1109/ASONAM.2016.7752353(945-948)Online publication date: Aug-2016
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