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

Sangho Suh, Sungbok Shin, Joonseok Lee, Chandan K. Reddy, Jaegul Choo

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)503-531
Number of pages29
JournalKnowledge and Information Systems
Issue number3
Publication statusPublished - 2018 Sept 1


  • Ensemble learning
  • Gradient boosting
  • Local weighting
  • Matrix factorization
  • Topic modeling

ASJC Scopus subject areas

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence


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