Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system

Tadafumi Kondo, Yuchi Kanzawa

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

Abstract

Various fuzzy co-clustering methods have been proposed for collaborative filtering; however, it is not clear which method is best in terms of accuracy. This paper proposes a recommender system that utilizes fuzzy co-clustering-based collaborative filtering and also evaluates four fuzzy co-clustering methods. The proposed system recommends optimal items to users using large-scale rating datasets. The results of numerical experiments conducted using one artificial dataset and two real datasets indicate that, the proposed method combined with a particular fuzzy co-clustering method is more accurate than conventional methods.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings
PublisherSpringer Verlag
Pages103-116
Number of pages14
Volume10571 LNAI
ISBN (Print)9783319674216
DOIs
Publication statusPublished - 2017
Event14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017 - Kitakyushu, Japan
Duration: 2017 Oct 182017 Oct 20

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10571 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017
CountryJapan
CityKitakyushu
Period17/10/1817/10/20

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Keywords

  • Co-clustering
  • Collaborative filtering
  • Fuzzy clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kondo, T., & Kanzawa, Y. (2017). Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system. In Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings (Vol. 10571 LNAI, pp. 103-116). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10571 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-319-67422-3_10