  Measuring the similarities between objects in information networks has
fundamental importance in recommendation systems, clustering and web search.
The existing metrics depend on the meta path or meta structure specified by
users. In this paper, we propose a stratified meta structure based similarity
$SMSS$ in heterogeneous information networks. The stratified meta structure can
be constructed automatically and capture rich semantics. Then, we define the
commuting matrix of the stratified meta structure by virtue of the commuting
matrices of meta paths and meta structures. As a result, $SMSS$ is defined by
virtue of these commuting matrices. Experimental evaluations show that the
proposed $SMSS$ on the whole outperforms the state-of-the-art metrics in terms
of ranking and clustering.
