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.

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
StatePublished - 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

Fingerprint

Collaborative filtering
Recommender systems
Optimal systems
Experiments

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. DOI: 10.1007/978-3-319-67422-3_10

Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system. / Kondo, Tadafumi; Kanzawa, Yuchi.

Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings. Vol. 10571 LNAI Springer Verlag, 2017. p. 103-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10571 LNAI).

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

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, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 10571 LNAI, Springer Verlag, pp. 103-116, 14th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2017, Kitakyushu, Japan, 17/10/18. DOI: 10.1007/978-3-319-67422-3_10
Kondo T, Kanzawa Y. 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. Springer Verlag. 2017. p. 103-116. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). Available from, DOI: 10.1007/978-3-319-67422-3_10
Kondo, Tadafumi ; Kanzawa, Yuchi. / Comparison of fuzzy co-clustering methods in collaborative filtering-based recommender system. Modeling Decisions for Artificial Intelligence - 14th International Conference, MDAI 2017, Proceedings. Vol. 10571 LNAI Springer Verlag, 2017. pp. 103-116 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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