GLDA-FP: Gaussian LDA model for forward prediction

Yunpeng Xiao, Liangyun Liu, Ming Xu, Haohan Wang, Yanbing Liu

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

Abstract

In social networks, information propagation is affected by diversity factors. In this work, we study the formation of forward behavior, map into multidimensional driving mechanisms and apply the behavioral and structural features to forward prediction. Firstly, by considering the effect of behavioral interest, user activity and network influence, we propose three driving mechanisms: interest-driven, habit-driven and structure-driven. Secondly, by taking advantage of the Latent Dirichlet allocation (LDA) model in dealing with problems of polysemy and synonymy, the traditional text modeling method is improved by Gaussian distribution and applied to user interest, activity and influence modeling. In this way, the user topic distribution for each dimension can be obtained regardless of whether the word is discrete or continuous. Moreover, the model can be extended using the pre-discretizing method which can help LDA detect the topic evolution automatically. By introducing time information, we can dynamically monitor user activity and mine the hidden behavioral habit. Finally, a novel model, Gaussian LDA, for forward prediction is proposed. The experimental results indicate that the model not only mine user latent interest, but also improve forward prediction performance effectively.

Original languageEnglish (US)
Title of host publicationBig Data – BigData 2018 - 7th International Congress, Held as Part of the Services Conference Federation, SCF 2018, Proceedings
EditorsLatifur Khan, Liang-Jie Zhang, Kisung Lee, Francis Y. Chin, C. L. Chen
PublisherSpringer
Pages124-139
Number of pages16
ISBN (Print)9783319943008
DOIs
StatePublished - 2018
Externally publishedYes
Event7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018 - Seattle, United States
Duration: Jun 25 2018Jun 30 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10968 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Congress on Big Data, BigData 2018 Held as Part of the Services Conference Federation, SCF 2018
Country/TerritoryUnited States
CitySeattle
Period6/25/186/30/18

Keywords

  • Forward prediction
  • LDA
  • Multidimensional driving mechanisms

ASJC Scopus subject areas

  • Theoretical Computer Science
  • General Computer Science

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