%0 Journal Article %A ZHAO Liang %A CHEN Pinghua %A LIAO Weiping %T Social Recommendation Based on Latent Factors of Social Network Users %D 2020 %R 10.3778/j.issn.1002-8331.1909-0339 %J Computer Engineering and Applications %P 169-174 %V 56 %N 24 %X

In order to solve the problem of the accuracy of social recommendation, a recommendation algorithm named SGCN-MF is proposed that fuses the latent factors of the user’s social network. The algorithm involves the influence of the implicit semantic information of the user in the social network on the result. user-project history information and user social networks are embedded using a graph convolutional neural network, then the user latent factors are integrated into the socialized recommendation model based on matrix decomposition, and finally the model parameters are trained by the gradient descent algorithm. Experiments on the Filmtrust, Ciao and Epinions dataset show that the algorithm can improve the accuracy of the recommendation results compared with the traditional social recommendation algorithm.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1909-0339