Collaborative Filtering Using Fuzzy Clustering for Categorical Multivariate Data Based on q-Divergence
Tadafumi Kondo, Yuchi Kanzawa · Journal of Advanced Computational Intelligence and Intelligent Informatics · 2019
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.