Basic Consideration of Collaborative Filtering Based on Rough Membership C-Means Clustering with Noise Rejection Mechanism

Abstract

In clustering-based collaborative filtering (CF), clusters of users with similar preference patterns are extracted, and highly preferred items within these clusters are recommended. Since the data used in CF tasks contain uncertainties due to human sensitivities, rough clustering based on rough set theory, which handles these uncertainties, is considered effective. Thus, RMCM-CF, a CF method based on rough membership C-means (RMCM) clustering, a type of rough clustering, has been proposed. In this study, we examine NRMCM-CF, a CF method based on noise RMCM, which incorporates a noise rejection mechanism into RMCM. In NRMCM-CF, uncertainty is considered using rough membership values, which are the proportions of clusters in the neighborhoods of objects, and objects far from any cluster center are removed as noise to achieve robust recommendations. Additionally, we verify the recommendation performance of the proposed method through numerical experiments using real-world datasets.