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Content-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems

  • Pierpaolo BasileEmail author
  • Cataldo Musto
  • Marco de Gemmis
  • Pasquale Lops
  • Fedelucio Narducci
  • Giovanni Semeraro
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 475)

Abstract

This paper provides an overview of the work done in the ESWC Linked Open Data-enabled Recommender Systems challenge, in which we proposed an ensemble of algorithms based on popularity, Vector Space Model, Random Forests, Logistic Regression, and PageRank, running on a diverse set of semantic features. We ranked 1st in the top-N recommendation task, and 3rd in the tasks of rating prediction and diversity.

Keywords

Random Forest Recommender System User Profile Vector Space Model Link Open Data 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Pierpaolo Basile
    • 1
    Email author
  • Cataldo Musto
    • 1
  • Marco de Gemmis
    • 1
  • Pasquale Lops
    • 1
  • Fedelucio Narducci
    • 1
  • Giovanni Semeraro
    • 1
  1. 1.Department of Computer ScienceUniversity of Bari Aldo MoroBariItaly

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