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

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

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.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 16th International Conference, MDAI 2019, Proceedings
EditorsVicenç Torra, Yasuo Narukawa, Gabriella Pasi, Marco Viviani
PublisherSpringer Verlag
Pages189-200
Number of pages12
ISBN (Print)9783030267728
DOIs
Publication statusPublished - 2019 Jan 1
Event16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019 - Milan, Italy
Duration: 2019 Sep 42019 Sep 6

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11676 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference16th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2019
CountryItaly
CityMilan
Period19/9/419/9/6

    Fingerprint

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Kanzawa, Y. (2019). On Collaborative Filtering with Possibilistic Clustering for Spherical Data Based on Tsallis Entropy. In V. Torra, Y. Narukawa, G. Pasi, & M. Viviani (Eds.), 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); Vol. 11676 LNAI). Springer Verlag. https://doi.org/10.1007/978-3-030-26773-5_17