GUO Jinchao and YANG Jigang. Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm[J]. Journal of Light Industry, 2020, 35(4): 88-95. doi: 10.12187/2020.04.012
Citation:
GUO Jinchao and YANG Jigang. Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm[J]. Journal of Light Industry, 2020, 35(4): 88-95.
doi:
10.12187/2020.04.012
Collaborative filtering algorithm based on the improved SVD algorithm and binary K-means clustering algorithm
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College of Electrical and Information Engineering, Zhengzhou University of Light Industry, Zhengzhou 450002, China
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Received Date:
2020-04-11
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Abstract
Aiming at the problem of large sparseness and poor scalability of traditional collaborative filtering algorithms,a collaborative filtering algorithm based on the improved singular value decomposition (SVD) algorithm and binary K-means clustering algorithm was proposed.The algorithm firstly used the improved SVD algorithm to reduce the dimensionality of the sparse user-item rating matrix to obtain the user implicit feature matrix,then used the binary K-means clustering algorithm to cluster similar users to improve the scalability of the algorithm,and finally used the nearest neighbor set score to correct the target user's score to make up for the error caused by the loss of user information due to matrix factorization.Experimental results on the MovieLens 100K data set showed that compared with the traditional user-based collaborative filtering algorithm,K-means clustering-based collaborative filtering algorithm and latent factor model (LFM) algorithm,this method could effectively improve the accuracy of recommendation results.
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