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

  • Katsufumi Tomobe
  • Masafumi Oyamada
  • Shinji Nakadai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10234)

Abstract

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.

Keywords

Relational model Topic model Link prediction Recommendation 

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Katsufumi Tomobe
    • 1
  • Masafumi Oyamada
    • 2
  • Shinji Nakadai
    • 2
  1. 1.Department of Mechanical EngineeringKeio UniversityKanagawaJapan
  2. 2.NEC CorporationKanagawaJapan

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