Extreme Learning Machine combining matrix factorization for collaborative filtering

Tianfeng Shang, Qing He, Fuzhen Zhuang, Zhongzhi Shi

Abstract: Collaborative Filtering (CF) is one of the most popular techniques for information filtering in recommendation systems. Currently, there are many linear and nonlinear regression algorithms for CF. However, to our knowledge, these regression algorithms may not give satisfactory results in some practical applications. In this paper, Extreme Learning Machine (ELM), which is famous with its fast speed and good performance in generalization, is firstly employed to build a nonlinear regression model for CF, namely ELM for CF (ELMCF) algorithm. Then by combining ELM and Weighted Nonnegative Matrix Tri-Factorization (WNMTF), which can alleviate the data sparsity problem of the user-item matrix, a new nonlinear regression model is proposed, namely Extreme Learning Machine Combining Matrix Factorization for Collaborative Filtering (CELMCF) algorithm, to construct regression based CF algorithms and improve the performance of recommendation systems. Experiments are conducted on several benchmark datasets from different application domains. Experimental results show that the proposed CELMCF algorithm outperforms some state-of-the-art regression based CF algorithms (including ELMCF algorithm, Linear Regression for CF (LRCF) algorithm and Memory based CF (MemCF) algorithm) more efficiently with the competitive effectiveness.