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

研究成果: Conference contribution

抄録

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

Fingerprint

Collaborative filtering
Fuzzy clustering
Entropy
Clustering algorithms
Experiments

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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

On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy. / Kanzawa, Yuchi.

Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings. 版 / Vicenç Torra; Yasuo Narukawa; Gabriella Pasi; Marco Viviani. Springer Verlag, 2019. p. 189-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); 巻 11676 LNAI).

研究成果: Conference contribution

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. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 巻. 11676 LNAI, Springer Verlag, pp. 189-200, 16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019, Milan, Italy, 19/9/4. https://doi.org/10.1007/978-3-030-26773-5_17
Kanzawa Y. On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy. : Torra V, Narukawa Y, Pasi G, Viviani M, 編集者, Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings. Springer Verlag. 2019. p. 189-200. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-030-26773-5_17
Kanzawa, Yuchi. / On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy. Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings. 編集者 / Vicenç Torra ; Yasuo Narukawa ; Gabriella Pasi ; Marco Viviani. Springer Verlag, 2019. pp. 189-200 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
@inproceedings{5b28a0e29aa74386b9a52989ae28756a,
title = "On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy",
abstract = "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.",
author = "Yuchi Kanzawa",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-3-030-26773-5_17",
language = "English",
isbn = "9783030267728",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "189--200",
editor = "Vicen{\cc} Torra and Yasuo Narukawa and Gabriella Pasi and Marco Viviani",
booktitle = "Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings",

}

TY - GEN

T1 - On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy

AU - Kanzawa, Yuchi

PY - 2019/1/1

Y1 - 2019/1/1

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=85072833276&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85072833276&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-26773-5_17

DO - 10.1007/978-3-030-26773-5_17

M3 - Conference contribution

AN - SCOPUS:85072833276

SN - 9783030267728

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 189

EP - 200

BT - Modeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings

A2 - Torra, Vicenç

A2 - Narukawa, Yasuo

A2 - Pasi, Gabriella

A2 - Viviani, Marco

PB - Springer Verlag

ER -