  Recently, deep neural network models for graph-structured data have been
demonstrating to be influential in recommendation systems. Graph Neural Network
(GNN), which can generate high-quality embeddings by capturing graph-structured
information, is convenient for the recommendation. However, most existing GNN
models mainly focus on the homogeneous graph. They cannot characterize
heterogeneous and complex data in the recommendation system. Meanwhile, it is
challenging to develop effective methods to mine the heterogeneity and latent
correlations in the graph. In this paper, we adopt Heterogeneous Attributed
Network (HAN), which involves different node types as well as rich node
attributes, to model data in the recommendation system. Furthermore, we propose
a novel graph neural network-based model to deal with HAN for Recommendation,
called HANRec. In particular, we design a component connecting potential
neighbors to explore the influence between neighbors and provide two different
strategies with the attention mechanism to aggregate neighbors' information.
The experimental results on two real-world datasets prove that HANRec
outperforms other state-of-the-art methods.
