This paper provides an overview of the work done in the 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 prediciton and diversity.

Aggregation Strategies for Linked Open Data-enabled Recommender Systems

BASILE, PIERPAOLO;MUSTO, CATALDO;DEGEMMIS, MARCO;LOPS, PASQUALE;NARDUCCI, FEDELUCIO;SEMERARO, Giovanni
2014

Abstract

This paper provides an overview of the work done in the 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 prediciton and diversity.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11586/132666
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