  The prosperous development of e-commerce has spawned diverse recommendation
systems. As a matter of fact, there exist rich and complex interactions among
various types of nodes in real-world recommendation systems, which can be
constructed as heterogeneous graphs. How learn representative node embedding is
the basis and core of the personalized recommendation system. Meta-path is a
widely used structure to capture the semantics beneath such interactions and
show potential ability in improving node embedding. In this paper, we propose
Heterogeneous Graph neural network for Recommendation (HGRec) which injects
high-order semantic into node embedding via aggregating multi-hops meta-path
based neighbors and fuses rich semantics via multiple meta-paths based on
attention mechanism to get comprehensive node embedding. Experimental results
demonstrate the importance of rich high-order semantics and also show the
potentially good interpretability of HGRec.
