  Heterogeneous information networks (HINs) are widely applied to
recommendation systems due to their capability of modeling various auxiliary
information with meta-paths. However, existing HIN-based recommendation models
usually fuse the information from various meta-paths by simple weighted sum or
concatenation, which limits performance improvement because it lacks the
capability of interest compositions among meta-paths. In this article, we
propose an HIN-based Interest Composition model for Recommendation (HicRec).
Specifically, user and item representations are learned with a graph neural
network on both the graph structure and features in each meta-path, and a
parameter sharing mechanism is utilized here to ensure that the user and item
representations are in the same latent space. Then, users' interests in each
item from each pair of related meta-paths are calculated by a combination of
the user and item representations. The composed user interests are obtained by
their single interest from both intra- and inter-meta-paths for recommendation.
Extensive experiments are conducted on three real-world datasets and the
results demonstrate that our proposed HicRec model outperforms the baselines.
