  Social recommendation based on social network has achieved great success in
improving the performance of recommendation system. Since social network
(user-user relations) and user-item interactions are both naturally represented
as graph-structured data, Graph Neural Networks (GNNs) have thus been widely
applied for social recommendation. In this work, we propose an end-to-end
heterogeneous global graph learning framework, namely Graph Learning Augmented
Heterogeneous Graph Neural Network (GL-HGNN) for social recommendation. GL-HGNN
aims to learn a heterogeneous global graph that makes full use of user-user
relations, user-item interactions and item-item similarities in a unified
perspective. To this end, we design a Graph Learner (GL) method to learn and
optimize user-user and item-item connections separately. Moreover, we employ a
Heterogeneous Graph Neural Network (HGNN) to capture the high-order complex
semantic relations from our learned heterogeneous global graph. To scale up the
computation of graph learning, we further present the Anchor-based Graph
Learner (AGL) to reduce computational complexity. Extensive experiments on four
real-world datasets demonstrate the effectiveness of our model.
