%0 Journal Article %A HE Haochen %A ZHANG Danhong %T Recommendation method based on multidimensional social relationship embedded deep graph neural network %D 2020 %R 10.11772/j.issn.1001-9081.2020040569 %J Journal of Computer Applications %P 2795-2803 %V 40 %N 10 %X The social recommendation system can alleviate the data sparsity and cold start problems in the recommendation system through the users' social attribute information, thereby improving the accuracy of the recommendation system. However, most social recommendation methods mainly aim at the single social network or linearly superimpose multiple social networks, making it difficult for the users' social attributes to fully participate in the calculation, so the accuracy of recommendation is limited. To solve this problem, a multi-network embedded graph neural network model was proposed to implement the recommendation in complex multidimensional social networks. In the model, a unified method was built to fuse the multidimensional complex networks composed of user-item, user-user and other relationships. Different types of multi-neighbors were aggregated to attribute to the node generation through attention mechanism, and multiple graph neural networks were combined to construct a graph neural network recommendation framework under multidimensional social relationships. In the proposed method, the entities in the recommendation system and their relationships were reflected by the topology structure, and the relevant information was calculated and updated continuously on the graph directly. It can be seen that the method is inductive, and avoids the problem of incomplete information utilization in traditional recommendation methods effectively. By comparing with related social recommendation algorithms, the experimental results show that the recommendation accuracy indicators such as Root Mean Square Error (RMSE) and Mean Absolute Error (MAE) of the proposed method are improved, and the method even has good accuracy on sparse data. %U http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2020040569