Effects of Various Granulation Methods in Collaborative Filtering Based on Rough Set C-Means Clustering

Abstract

Collaborative filtering (CF) is a technique for realizing recommender systems found in e-commerce sites and video streaming sites. Appropriate content recommendations to individual users will improve usability, purchase rates, video ratings, and corporate profits. Clustering is a technique for automatically classifying and summarizing the data by extracting clusters composed of similar objects. Clustering-based CF extracts clusters of users with similar interests and preferences, and recommends highly preferred contents in the cluster to each user. Rough clustering is a promising approach for dealing with the uncertainty of belonging of object to clusters. In this study, we verify the effects of various granulation methods in rough set C-means clustering-based collaborative filtering (RSCM-CF) through numerical experiments using a real-world dataset, namely, MovieLens dataset.