JACIII Vol.27 No.6 pp. 1070-1078
doi: 10.20965/jaciii.2023.p1070

Research Paper:

Collaborative Filtering with q-Divergence-Based Relational Fuzzy c-Means Clustering

Yuchi Kanzawa* ORCID Icon, Kaoru Atsuta**, and Genki Midorikawa***

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

**JFE Systems, Inc.
1-2-3 Shibaura, Minato-ku, Tokyo 105-0023, Japan

***PMO Nihonbashi Kayabacho
3-11-10 Nihonbashi Kayabacho, Chuo-ku, Tokyo 103-0025, Japan

March 5, 2023
May 25, 2023
November 20, 2023
collaborative filtering, fuzzy clustering, relational data

This paper presents a collaborative filtering (CF) algorithm for a recommendation system motivated by the need for higher recommendation accuracy. Cluster analysis captures information from a group of users such that users within a given cluster are more similar to each other than to those in other clusters. Therefore, clustering helps to detect similar users. However, inconsistent similarity measures have been applied during the clustering and prediction stages in the literature. Hence, this study resolves such discrepancies through the proposed CF algorithm, which uses fuzzy clustering for relational data such that a common similarity measure is applied to both the clustering and prediction stages. Experiments were conducted with ten datasets based on an artificial dataset and 40 datasets based on eight real datasets to demonstrate that the proposed algorithm could achieve a higher CF accuracy than conventional methods.

Cite this article as:
Y. Kanzawa, K. Atsuta, and G. Midorikawa, “Collaborative Filtering with q-Divergence-Based Relational Fuzzy c-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.6, pp. 1070-1078, 2023.
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Last updated on Jul. 19, 2024