Performance comparison of collaborative filtering using fuzzy clustering for spherical data

Tadafumi Kondo, Yuchi Kanzawa

研究成果: Conference contribution

1 被引用数 (Scopus)

抄録

GroupLens is the representative neighborhood-based algorithm for collaborative filtering task, where the definition of 'neighborhood' is heuristic. This study proposes determining the neighborhood using fuzzy clustering for spherical data, and compares three fuzzy clustering for spherical data been proposed with GroupLens algorithm, using two real datasets. The experimental result shows that the proposal achieved higher recommendation accuracy for all two datasetsand that all three clustering algorithms help determining more adequate neighbor-hood than the conventionally determined neighborhood.

本文言語English
ホスト出版物のタイトルProceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
出版社Institute of Electrical and Electronics Engineers Inc.
ページ644-647
ページ数4
ISBN(電子版)9781538626337
DOI
出版ステータスPublished - 2019 5 15
イベントJoint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018 - Toyama, Japan
継続期間: 2018 12 52018 12 8

出版物シリーズ

名前Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018

Conference

ConferenceJoint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
国/地域Japan
CityToyama
Period18/12/518/12/8

ASJC Scopus subject areas

  • 人間とコンピュータの相互作用
  • 論理
  • 人工知能
  • 計算理論と計算数学
  • コンピュータ サイエンスの応用
  • 理論的コンピュータサイエンス

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