User correlation discovery and dynamical profiling based on social streams

Xiaokang Zhou, Qun Jin

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

Abstract

In this study, we try to discover the potential and dynamical user correlations using those reorganized social streams in accordance with users' current interests and needs, in order to assist the information seeking process. We develop a mechanism to build a Dynamical Socialized User Networking (DSUN) model, and define a set of measures (such as interest degree, and popularity degree) and concepts (such as complementary tie, weak tie, and strong tie), which can discover and represent users' current profiling and dynamical correlations. The corresponding algorithms are developed respectively. Based on these, we finally discuss an application scenario of the DSUN model with experiment results.

Original languageEnglish
Title of host publicationActive Media Technology - 8th International Conference, AMT 2012, Proceedings
Pages53-62
Number of pages10
DOIs
Publication statusPublished - 2012 Dec 6
Event8th International Conference on Active Media Technology, AMT 2012 - Macau, China
Duration: 2012 Dec 42012 Dec 7

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7669 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Conference on Active Media Technology, AMT 2012
CountryChina
CityMacau
Period12/12/412/12/7

Keywords

  • Information Seeking
  • SNS
  • Social Stream
  • Stream Metaphor
  • User Profiling

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Zhou, X., & Jin, Q. (2012). User correlation discovery and dynamical profiling based on social streams. In Active Media Technology - 8th International Conference, AMT 2012, Proceedings (pp. 53-62). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7669 LNCS). https://doi.org/10.1007/978-3-642-35236-2_6