  Most real-world data can be modeled as heterogeneous information networks
(HINs) consisting of vertices of multiple types and their relationships. Search
for similar vertices of the same type in large HINs, such as bibliographic
networks and business-review networks, is a fundamental problem with broad
applications. Although similarity search in HINs has been studied previously,
most existing approaches neither explore rich semantic information embedded in
the network structures nor take user's preference as a guidance.
  In this paper, we re-examine similarity search in HINs and propose a novel
embedding-based framework. It models vertices as low-dimensional vectors to
explore network structure-embedded similarity. To accommodate user preferences
at defining similarity semantics, our proposed framework, ESim, accepts
user-defined meta-paths as guidance to learn vertex vectors in a user-preferred
embedding space. Moreover, an efficient and parallel sampling-based
optimization algorithm has been developed to learn embeddings in large-scale
HINs. Extensive experiments on real-world large-scale HINs demonstrate a
significant improvement on the effectiveness of ESim over several
state-of-the-art algorithms as well as its scalability.
