Using Graph Metrics for Linked Open Data Enabled Recommender Systems

  • Petar RistoskiEmail author
  • Michael Schuhmacher
  • Heiko Paulheim
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 239)


Linked Open Data has been recognized as a useful source of background knowledge for building content-based recommender systems. While many existing approaches transform that data into a propositional form, we investigate how the graph nature of Linked Open Data can be exploited when building recommender systems. In particular, we use path lengths, the K-Step Markov approach, as well as weighted NI paths to compute item relevance and perform a content-based recommendation. An evaluation on the three tasks of the 2015 LOD-RecSys challenge shows that the results are promising, and, for cross-domain recommendations, outperform collaborative filtering.


Linked Open Data Recommender systems Graph metrics Cross-domain recommendation 



The work presented in this paper has been partly funded by the German Research Foundation (DFG) under grant number PA 2373/1-1 (Mine@LOD). Part of this work was performed on the computational resource bwUniCluster funded by the Ministry of Science, Research and the Arts Baden-Württemberg and the Universities of the State of Baden-Württemberg, Germany, within the framework program bwHPC. We would like to thank our colleague Robert Meusel for his valuable contribution to our system.


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Petar Ristoski
    • 1
    Email author
  • Michael Schuhmacher
    • 1
  • Heiko Paulheim
    • 1
  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany

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