Collaborative filtering using fuzzy clustering for categorical multivariate data based on q-divergence

Tadafumi Kondo, Yuchi Kanzawa

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

In this study, a collaborative filtering method that uses fuzzy clustering and is based on q-divergence is proposed for categorical multivariate data. The results of experiments conducted on an artificial dataset indicate that the proposed method is more effective than the conventional one if the number of clusters and the initial setting are adequately set. Furthermore, the results of the experiments conducted on three real datasets indicate that the proposed method outperforms the conventional method in terms of recommendation accuracy as well.

Original languageEnglish
Pages (from-to)493-501
Number of pages9
JournalJournal of Advanced Computational Intelligence and Intelligent Informatics
Volume23
Issue number3
DOIs
Publication statusPublished - 2019 May 1

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Collaborative filtering
Fuzzy clustering
Experiments

Keywords

  • Categorical multivariate data
  • Collaborative filtering
  • Fuzzy clustering
  • Q-divergence

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Cite this

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