Abstract
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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Acknowledgements
The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210111. The Ministry of Public Security Technical Research Plan, under Grant no. 2016JSYJB38. The Scientific and Technological Research Program of Henan Province, China under Grant no. 172102210441.
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Zhang, Tw., Li, Wp., Wang, L. et al. Social recommendation algorithm based on stochastic gradient matrix decomposition in social network. J Ambient Intell Human Comput 11, 601–608 (2020). https://doi.org/10.1007/s12652-018-1167-7
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DOI: https://doi.org/10.1007/s12652-018-1167-7