  Heterogeneous information network (HIN) embedding has recently attracted much
attention due to its effectiveness in dealing with the complex heterogeneous
data. Meta path, which connects different object types with various semantic
meanings, is widely used by existing HIN embedding works. However, several
challenges have not been addressed so far. First, different meta paths convey
different semantic meanings, while existing works assume that all nodes share
same weights for meta paths and ignore the personalized preferences of
different nodes on different meta paths. Second, given a meta path, nodes in
HIN are connected by path instances while existing works fail to fully explore
the differences between path instances that reflect nodes' preferences in the
semantic space. rTo tackle the above challenges, we propose aHierarchical
Attentive Heterogeneous information network Embedding (HAHE) model to capture
the personalized preferences on meta paths and path instances in each semantic
space. As path instances are based on a particular meta path, a hierarchical
attention mechanism is naturally utilized to model the personalized preference
on meta paths and path instances. Extensive experiments on several real-world
datasets show that our proposed \model model significantly outperforms the
state-of-the-art methods in terms of various data mining tasks.
