  In this paper, we study network representation learning for tripartite
heterogeneous networks which learns node representation features for networks
with three types of node entities. We argue that tripartite networks are common
in real world applications, and the essential challenge of the representation
learning is the heterogeneous relations between various node types and links in
the network. To tackle the challenge, we develop a tripartite heterogeneous
network embedding called TriNE. The method considers unique user-item-tag
tripartite relationships, to build an objective function to model explicit
relationships between nodes (observed links), and also capture implicit
relationships between tripartite nodes (unobserved links across tripartite node
sets). The method organizes metapath guided random walks to create
heterogeneous neighborhood for all node types in the network. This information
is then utilized to train a heterogeneous skip-gram model based on a joint
optimization. Experiments on real-world tripartite networks validate the
performance of TriNE for the online user response prediction using embedding
node features.
