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
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.
-  R. Paul, I. Neophytos, S. Mitesh, S. Peter, and R. Jhon, “GroupLens: An Open Architecture for Collaborative Filtering of Netnews,” Proc. Computer Supported Cooperative Work of the ACM, pp. 175-186, 1994.
-  B. Sarwar, G. Karypis, and J. Riedl, “Item-Based Collaborative Filtering Recommendation Algorithms,” Proc. the 10th Int. Conf. on World Wide Web, pp. 285-295, 2001.
-  J. L. Herlocker, J. A. Konstan, A. Borchers, and J. Riedl, “An algorithmic framework for performing collaborative filtering,” Proc. the 22nd Annual Int. ACM SIGIR Conf. on Research and Development in Information Retrieval, pp. 230-237, 1999.
-  K. Honda, “Fuzzy Co-Clustering and Application to Collaborative Filtering,” Lecture Notes in Computer Science (LNCS), Vol.9978, pp. 16-23, 2016.
-  T. Kondo and Y. Kanzawa, “Fuzzy Clustering Methods for Categorical Multivariate Data Based on q-Divergence,” J. Adv. Comput. Intell. Intell. Inform., Vol.22, No.4, pp. 524-536, 2018.
-  K. Honda, S. Oshio, and A. Notsu, “Fuzzy Co-Clustering Induced by Multinomial Mixture Models,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.6, pp. 717-726, 2015.
-  GroupLens: MovieLens, http://grouplens.org/datasets/movielens/ [accessed July 1, 2016]
-  C. Ziegler, BookCrossing, http://www2.informatik.uni-freiburg.de/cziegler/BX/ [accessed July 1, 2016]
-  K. Goldberg, Jester, http://eigentaste.berkeley.edu/dataset/ [accessed July 1, 2016]
-  J. A. Swets, “ROC Analysis Applied to the Evaluation of Medical Imaging Techniques,” Proc. Investigative Radiology, Vol.14, pp. 109-121, 1979.
-  J. A. Hanley and B. J. McNeil, “The meaning and use of the area under a receiver operating characteristic (ROC) curve,” Proc. Radiology, Vol.143, pp. 29-36, 1982.
-  D. Arthur and S. Vassilvitskii, “k-means++: the advantages of careful seeding,” Proc. the 8th Annual ACM-SIAM Symp. on Discrete Algorithms, pp. 1027-1035, 2007.
-  Y. Kanzawa, “Bezdek-Type Fuzzified Co-Clustering Algorithm,” J. Adv. Comput. Intell. Intell. Inform., Vol.19, No.6, pp. 852-860, 2015.
-  K. Rose, E. Gurewitz, and G. Fox, “A deterministic annealing approach to clustering,” Pattern Recognition Letters, Vol.11, pp. 589-594, 1990.
-  Y. Kanzawa, “On Possibilistic Clustering Methods Based on Shannon/Tsallis-Entropy for Spherical Data and Categorical Multivariate Data,” Lecture Notes in Artificial Intelligence, Vol.9321, pp. 115-128, 2015.