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Social recommendation based on social network has achieved great success inimproving the performance of recommendation system. Since social network(user-user relations) and user-item interactions are both naturally representedas graph-structured data, Graph Neural Networks (GNNs) have thus been widelyapplied for social recommendation. In this work, we propose an end-to-endheterogeneous global graph learning framework, namely Graph Learning AugmentedHeterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNNaims to learn a heterogeneous global graph that makes full use of user-userrelations, user-item interactions and item-item similarities in a unifiedperspective. To this end, we design a Graph Learner (GL) method to learn andoptimize user-user and item-item connections separately. Moreover, we employ aHeterogeneous Graph Neural Network (HGNN) to capture the high-order complexsemantic relations from our learned heterogeneous global graph. To scale up thecomputation of graph learning, we further present the Anchor-based GraphLearner (AGL) to reduce computational complexity. Extensive experiments on fourreal-world datasets demonstrate the effectiveness of our model.

Graph Learning Augmented Heterogeneous Graph Neural Network for Social Recommendation