Abstract: The powerful ability of graph neural network to process non-Euclidean spatial data has prompted more and more people to pay attention to its application in the recommendation field. However, most of the existing recommendation models based on graph neural networks still use several adjacency matrices to represent heterogeneous information such as all kind of nodes or edge attributes, and fail to make full use of the interaction of heterogeneous information. Therefore, this paper proposes a new graph neural network recommendation model, which models the rich interactions between all information entities as heterogeneous graph and uses the dense subgraph sampling strategy for sampling the subgraphs of heterogeneous graph. In addition, the multi-task learning method is added to the model to jointly optimize the link prediction and recommendation tasks, so that the model learns a better node representation and effectively improves the recommendation results. Experiments on two public datasets show that, compared with the baseline models, the proposed model improves the performance of the Top-N recommendation task. 

Key words: recommender system, graph neural network, multi-task learning