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.

1. [1] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. of the 1994 ACM Conf. on Computer Supported Cooperative Work, pp. 175-186, 1994. https://doi.org/10.1145/192844.192905
2. [2] J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An Algorithmic Framework for Performing Collaborative Filtering,” Proc. of the 22nd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 230-237, 1999. https://doi.org/10.1145/312624.312682
3. [3] K. Honda, “Fuzzy Co-Clustering and Application to Collaborative Filtering,” V.-N. Huynh et al. (Eds.), “IUKM 2016,” LNAI, Vol.9978, pp. 16-23, Springer, 2016. https://doi.org/10.1007/978-3-319-49046-5_2
4. [4] 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. https://doi.org/10.20965/jaciii.2019.p0493
5. [5] T. Kondo and Y. Kanzawa, “Collaborative Filtering with q-Divergence-Based Fuzzy Clustering for Spherical Data,” J. Ambient Intell. Humanized Comput., 2021. https://doi.org/10.1007/s12652-021-03128-6
6. [6] Y. Kanzawa, “q-Divergence-Based Relational Fuzzy c-Means Clustering,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.1, pp. 34-43, 2018. https://doi.org/10.20965/jaciii.2018.p0034
7. [7] M. Higashi, T. Kondo, and Y. Kanzawa, “Fuzzy Clustering Method for Spherical Data Based on q-Divergence,” J. Adv. Comput. Intell. Intell. Inform., Vol.23, No.3, pp. 561-570, 2019. https://doi.org/10.20965/jaciii.2019.p0561
8. [8] A. Mead, “Review of the Development of Multidimensional Scaling Methods,” J. R. Stat. Soc. Ser. D, Vol.41, No.1, pp. 27-39, 1992. https://doi.org/10.2307/2348634
9. [9] J. A. Hanley and B. J. McNeil, “The Meaning and Use of the Area Under a Receiver Operating Characteristic (ROC) Curve,” Radiology, Vol.143, pp. 29-36, 1982. https://doi.org/10.1148/radiology.143.1.7063747
10. [10] P. Massa, K. Souren, M. Salvetti, and D. Tomasoni, “Trustlet: Open Research on Trust Metrics,” Scalable Comput Practice Experience, Vol.9, No.4, pp. 341-351, 2008.
11. [11] L. Brozovsky and V. Petricek, “Recommender System for Online Dating Service,” arXiv:cs/0703042, 2007. https://doi.org/10.48550/arXiv.cs/0703042
12. [12] C.-N. Ziegler, S. M. McNee, J. A. Konstan, and G. Lausen, “Improving recommendation lists through topic diversification,” Proc. of the 14th Int. Conf. on World Wide Web, pp. 22-32, 2005. https://doi.org/10.1145/1060745.1060754
13. [13] F. M. Harper and J. A. Konstan, “The MovieLens Datasets: History and Context,” ACM Trans. Interact. Intell. Syst., Vol.5, No.4, Article No.19, 2015. https://doi.org/10.1145/2827872
14. [14] The Jester Dataset. https://goldberg.berkeley.edu/jester-data/ [Accessed March 4, 2023]
15. [15] SUSHI Preference Data Sets. https://www.kamishima.net/sushi/ [Accessed March 4, 2023]
16. [16] Netflix Prize Data Sets. https://www.kaggle.com/datasets/netflix-inc/netflix-prize-data [Accessed March 4, 2023]
17. [17] I. J. Sledge, J. C. Bezdek, T. C. Havens, and J. M. Keller, “Relational Generalizations of Cluster Validity Indices,” IEEE Trans. Fuzzy Syst., Vol.18, No.4, pp. 771-786, 2010. https://doi.org/10.1109/TFUZZ.2010.2048114