  Due to the flexibility in modelling data heterogeneity, heterogeneous
information network (HIN) has been adopted to characterize complex and
heterogeneous auxiliary data in top-$N$ recommender systems, called
\emph{HIN-based recommendation}. HIN characterizes complex, heterogeneous data
relations, containing a variety of information that may not be related to the
recommendation task. Therefore, it is challenging to effectively leverage
useful information from HINs for improving the recommendation performance. To
address the above issue, we propose a Curriculum pre-training based
HEterogeneous Subgraph Transformer (called \emph{CHEST}) with new \emph{data
characterization}, \emph{representation model} and \emph{learning algorithm}.
  Specifically, we consider extracting useful information from HIN to compose
the interaction-specific heterogeneous subgraph, containing both sufficient and
relevant context information for recommendation. Then we capture the rich
semantics (\eg graph structure and path semantics) within the subgraph via a
heterogeneous subgraph Transformer, where we encode the subgraph with
multi-slot sequence representations. Besides, we design a curriculum
pre-training strategy to provide an elementary-to-advanced learning process, by
which we smoothly transfer basic semantics in HIN for modeling user-item
interaction relation.
  Extensive experiments conducted on three real-world datasets demonstrate the
superiority of our proposed method over a number of competitive baselines,
especially when only limited training data is available.
