Link Prediction for Isolated Nodes in Heterogeneous Network by Topic-Based Co-clustering

PAKDD 2017

This paper presents a new probabilistic generative model (PGM) that predicts links for isolated nodes in a heterogeneous network using textual data. In conventional PGMs, a link between two nodes is predicted on the basis of the nodes’ other existing links. This method makes it difficult to predict links for isolated nodes, which happens when new items are recommended. In this study, we first naturally expand the relational topic model (RTM) to a heterogeneous network (Hetero-RTM). However, this simple extension degrades performance in a link prediction for existing nodes. We present a new model called the Grouped Hetero-RTM that has both latent topics and latent clusterings. Through intensive experiments that simulate real recommendation problems, the Grouped Hetero-RTM outperforms baseline methods at predicting links for isolated nodes. This model, furthermore, performs as effectively as the stochastic block model in the link prediction for existing nodes. We also find that the Grouped Hetero-RTM is effective for various textual data such as item reviews and movie descriptions.