  Heterogeneous Information Networks (HIN) has been widely used in recommender
systems (RSs). In previous HIN-based RSs, meta-path is used to compute the
similarity between users and items. However, existing meta-path based methods
only consider first-order relations, ignoring higher-order relations among the
nodes of \textit{same} type, captured by \textit{motifs}. In this paper, we
propose to use motifs to capture higher-order relations among nodes of same
type in a HIN and develop the motif-enhanced meta-path (MEMP) to combine
motif-based higher-order relations with edge-based first-order relations. With
MEMP-based similarities between users and items, we design a recommending model
MoHINRec, and experimental results on two real-world datasets, Epinions and
CiaoDVD, demonstrate its superiority over existing HIN-based RS methods.
