Social recommendation algorithm based on stochastic gradient matrix decomposition in social network
The revenue of an e-commerce system is affected directly by the prediction accuracy of recommendation system. Although recommendation systems have been comprehensively analyzed in the past decade, the study of social-based recommendation systems just started. In this paper, aiming at providing a gen...
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Published in | Journal of ambient intelligence and humanized computing Vol. 11; no. 2; pp. 601 - 608 |
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Main Authors | , , , |
Format | Journal Article |
Language | English |
Published |
Berlin/Heidelberg
Springer Berlin Heidelberg
01.02.2020
Springer Nature B.V |
Subjects | |
Online Access | Get full text |
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Summary: | The revenue of an e-commerce system is affected directly by the prediction accuracy of recommendation system. Although recommendation systems have been comprehensively analyzed in the past decade, the study of social-based recommendation systems just started. In this paper, aiming at providing a general method for improving recommendation systems by incorporating social network information, we propose a social recommendation algorithm based on stochastic gradient matrix decomposition in social network so as to improve the prediction accuracy. This paper considered the social network as auxiliary information, and proposed a matrix factorization based on social recommendation algorithm, which systematically illustrate how to design a matrix factorization objective function with social regularization. It constructed a matrix with the social network and the user scoring matrix, and proposed a stochastic gradient descent algorithm for matrix factorization. The empirical analysis on two large datasets demonstrates our proposed algorithm has lower prediction error, and is obviously better than other state-of-the-art methods. |
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ISSN: | 1868-5137 1868-5145 |
DOI: | 10.1007/s12652-018-1167-7 |