Abstract：With the development of social networks, social recommendation algorithms are widely used. Existing recommendation algorithms often only introduce one kind of social relationship into the recommendation system, but in reality there are multiple social relationships between users. Based on the multi-subnet composite complex network model and the shared user characteristic matrix, this paper proposes a matrix decomposition recommendation algorithm based on the multi-relational social network. Through the analysis of experimental results on the Epinions data set, the accuracy evaluation indexes MAE, RMSE and NMAE increased by 34%, 27% and 7% respectively. This proves that the matrix factorization recommendation algorithm of multi-relational social networks can effectively improve the accuracy of recommendation.
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