On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy

研究成果: Conference contribution

抜粋

This paper proposes a collaborative filtering (CF) method using possibilistic clustering for spherical data based on Tsallis entropy. This study was motivated by a previous work, which showed that adopting fuzzy clustering for spherical data in CF tasks provided better recommendation accuracy than fuzzy clustering for categorical-multivariate data. Moreover, possibilistic clustering algorithms are naturally more robust to noise than fuzzy clustering. The results of experiments conducted on an artificial dataset and one real dataset indicate that the proposed method is better than the conventional methods in terms of recommendation accuracy.

元の言語English
ホスト出版物のタイトルModeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings
編集者Vicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani
出版者Springer Verlag
ページ189-200
ページ数12
ISBN(印刷物)9783030267728
DOI
出版物ステータスPublished - 2019 1 1
イベント16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019 - Milan, Italy
継続期間: 2019 9 42019 9 6

出版物シリーズ

名前Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11676 LNAI
ISSN(印刷物)0302-9743
ISSN(電子版)1611-3349

Conference

Conference16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019
Italy
Milan
期間19/9/419/9/6

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • これを引用

    Kanzawa, Y. (2019). On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy. : V. Torra, Y. Narukawa, G. Pasi, & M. Viviani (版), Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings (pp. 189-200). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻数 11676 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-26773-5_17