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.
Content-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems
BASILE, PIERPAOLO;MUSTO, CATALDO;DEGEMMIS, MARCO;LOPS, PASQUALE;NARDUCCI, FEDELUCIO;SEMERARO, Giovanni
2014-01-01
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.File in questo prodotto:
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