Collaborative filtering recommendation algorithm based on the self-similarity matrix

doi:10.3969/j.issn.1000-5641.2018.04.012

Abstract

Abstract: A collaborative filtering recommendation algorithm based on self-similar matrices is put forward for the noise problem in the proposed system. In this paper, self-similar matrices are selected as primitive matrices, and the sliding window is chosen as the row vector and column vector of the score. The new score matrix is obtained to preprocess the original scoring matrix to establish the linear relationship between the scoring value and the self-similar matrices. The new scoring matrix preserves the original matrix of scoring information, while weakening the impact of noise data on the recommended system. Experiments show that the pre-processing of the original matrix effectively alleviates the impact of noise in the scoring matrix and improves the performance of the proposed system.

Key words: recommendation system, collaborative filtering, noise data, self-similarity matrix, pre-processing

CLC Number:

TP391

ZHANG Wei, ZHENG Jun, PANG Jiao-na, BAI Yue. Collaborative filtering recommendation algorithm based on the self-similarity matrix[J]. Journal of East China Normal University(Natural Sc, 2018, 2018(4): 120-128,146.

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