Canopy-K-means Combined Collaborative Filtering Using RMSE-minimization

被引：0

作者：

Kuan, Sao-, I ^{[1
]}

Kim, Jongmin ^{[2
]}

Kwon, Oh-Heum ^{[2
]}

Song, Ha-Joo ^{[2
]}

机构：

[1] Pukyong Natl Univ, Dept IT Convergence & Applicat Engn, Macau, Peoples R China

[2] Pukyong Natl Univ, Dept IT Convergence & Applicat Engn, Busan, South Korea

来源：

2022 IEEE INTERNATIONAL CONFERENCE ON BIG DATA AND SMART COMPUTING (IEEE BIGCOMP 2022) | 2022年

关键词：

recommendation system; combined collaborative filtering; RMSE-minimization; canopy clustering; k-means clustering;

D O I：

10.1109/BigComp54360.2022.00016

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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 itembased 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 Netflix 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).

引用

页码：31 / 34

页数：4