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Knowledge and Information Systems

, Volume 56, Issue 3, pp 503–531 | Cite as

Localized user-driven topic discovery via boosted ensemble of nonnegative matrix factorization

  • Sangho Suh
  • Sungbok Shin
  • Joonseok Lee
  • Chandan K. Reddy
  • Jaegul Choo
Regular Paper
  • 383 Downloads

Abstract

Nonnegative matrix factorization (NMF) has been widely used in topic modeling of large-scale document corpora, where a set of underlying topics are extracted by a low-rank factor matrix from NMF. However, the resulting topics often convey only general, thus redundant information about the documents rather than information that might be minor, but potentially meaningful to users. To address this problem, we present a novel ensemble method based on nonnegative matrix factorization that discovers meaningful local topics. Our method leverages the idea of an ensemble model, which has shown advantages in supervised learning, into an unsupervised topic modeling context. That is, our model successively performs NMF given a residual matrix obtained from previous stages and generates a sequence of topic sets. The algorithm we employ to update is novel in two aspects. The first lies in utilizing the residual matrix inspired by a state-of-the-art gradient boosting model, and the second stems from applying a sophisticated local weighting scheme on the given matrix to enhance the locality of topics, which in turn delivers high-quality, focused topics of interest to users. We subsequently extend this ensemble model by adding keyword- and document-based user interaction to introduce user-driven topic discovery.

Keywords

Topic modeling Ensemble learning Matrix factorization Gradient boosting Local weighting 

Notes

Acknowledgements

This work was supported in part by the National Science Foundation Grants IIS-1707498, IIS-1619028, and IIS-1646881 and by Basic Science Research Program through the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP) (No. NRF-2016R1C1B2015924). Any opinions, findings, and conclusions or recommendations expressed here are those of the authors and do not necessarily reflect the views of funding agencies.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Sangho Suh
    • 1
  • Sungbok Shin
    • 2
  • Joonseok Lee
    • 3
  • Chandan K. Reddy
    • 4
  • Jaegul Choo
    • 2
  1. 1.David R. Cheriton School of Computer ScienceUniversity of WaterlooWaterlooCanada
  2. 2.Department of Computer Science and EngineeringKorea UniversitySeoulSouth Korea
  3. 3.Google ResearchMountain ViewUSA
  4. 4.Department of Computer ScienceVirginia TechArlingtonUSA

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