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JACIII Vol.23 No.3 pp. 493-501
doi: 10.20965/jaciii.2019.p0493
(2019)

Paper:

Collaborative Filtering Using Fuzzy Clustering for Categorical Multivariate Data Based on q-Divergence

Tadafumi Kondo and Yuchi Kanzawa

Shibaura Institute of Technology
3-7-5 Toyosu, Koto, Tokyo 135-8548, Japan

Received:
January 26, 2018
Accepted:
December 5, 2018
Published:
May 20, 2019
Keywords:
collaborative filtering, fuzzy clustering, categorical multivariate data, q-divergence
Abstract

In this study, a collaborative filtering method that uses fuzzy clustering and is based on q-divergence is proposed for categorical multivariate data. The results of experiments conducted on an artificial dataset indicate that the proposed method is more effective than the conventional one if the number of clusters and the initial setting are adequately set. Furthermore, the results of the experiments conducted on three real datasets indicate that the proposed method outperforms the conventional method in terms of recommendation accuracy as well.

Cite this article as:
T. Kondo and Y. Kanzawa, “Collaborative Filtering Using Fuzzy Clustering for Categorical Multivariate Data Based on q-Divergence,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 493-501, 2019.
Data files:
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Last updated on Nov. 18, 2019