A Semantic-Rich Similarity Measure in Heterogeneous Information Networks

Abstract: 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.