Canopy�K-means Combined Collaborative Filtering Using RMSE-minimization
Sao-I Kuan, Jongmin Kim, Oh‐Heum Kwon, Ha-Joo Song · 2022
Collaborative filtering is one of the most conventional algorithm in recommendation system. However, CF suffers from data sparsity and scalability issues. Thus, we propose Canopy–K-means Combined Collaborative Filtering (CK-ComCF) to solve the challenge of data sparsity and scalability. In particular, the prediction outcomes of user-based CF and item-based CF are integrated using a weighting approach, which is based on the root-mean-square error minimization. Experiment results based on two real-life datasets of MovieLens and Net-flix Prize demonstrate that the proposed RMSE-minimization method outperforms the traditional CF methods, improving the accuracy by 64.24% (UbCF with MovieLens) and 13.72% (IbCF with Netflix Prize). The proposed CKComCF model outperforms the existing improved CF method, reducing the calculation time by 41.84% (MovieLens) and 64.77% (Netflix Prize).