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A Hybrid Multi-strategy Recommender System Using Linked Open Data

  • Petar Ristoski
  • Eneldo Loza Mencía
  • Heiko Paulheim
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

In this paper, we discuss the development of a hybrid multi-strategy book recommendation system using Linked Open Data. Our approach builds on training individual base recommenders and using global popularity scores as generic recommenders. The results of the individual recommenders are combined using stacking regression and rank aggregation. We show that this approach delivers very good results in different recommendation settings and also allows for incorporating diversity of recommendations.

Keywords

Linked Open Data Hybrid recommender systems Stacking 

Notes

Acknowledgements

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).

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Petar Ristoski
    • 1
  • Eneldo Loza Mencía
    • 2
  • Heiko Paulheim
    • 1
  1. 1.Research Group Data and Web ScienceUniversity of MannheimMannheimGermany
  2. 2.Knowledge Engineering GroupTechnische Universität DarmstadtDarmstadtGermany

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