  Social recommendation which aims to leverage social connections among users
to enhance the recommendation performance. With the revival of deep learning
techniques, many efforts have been devoted to developing various neural
network-based social recommender systems, such as attention mechanisms and
graph-based message passing frameworks. However, two important challenges have
not been well addressed yet: (i) Most of existing social recommendation models
fail to fully explore the multi-type user-item interactive behavior as well as
the underlying cross-relational inter-dependencies. (ii) While the learned
social state vector is able to model pair-wise user dependencies, it still has
limited representation capacity in capturing the global social context across
users. To tackle these limitations, we propose a new Social Recommendation
framework with Hierarchical Graph Neural Networks (SR-HGNN). In particular, we
first design a relation-aware reconstructed graph neural network to inject the
cross-type collaborative semantics into the recommendation framework. In
addition, we further augment SR-HGNN with a social relation encoder based on
the mutual information learning paradigm between low-level user embeddings and
high-level global representation, which endows SR-HGNN with the capability of
capturing the global social contextual signals. Empirical results on three
public benchmarks demonstrate that SR-HGNN significantly outperforms
state-of-the-art recommendation methods. Source codes are available at:
https://github.com/xhcdream/SR-HGNN.
