关键词: 社会化推荐, 矩阵分解, 潜在因子, 社交网络, 图卷积神经网络

Abstract:

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.

Key words: social recommendation, matrix factorization, latent factors, social networks, graph convolutional neural network