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.

CContent-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems / Basile, Pierpaolo; Musto, Cataldo; de Gemmis, Marco; Lops, Pasquale; Narducci, Fedelucio; Semeraro, Giovanni. - STAMPA. - 475:(2014), pp. 163-169. [10.1007/978-3-319-12024-9_21]

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

Fedelucio Narducci;
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.
2014
Semantic Web Evaluation Challenge SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers
978-3-319-12023-2
Springer
CContent-Based Recommender Systems + DBpedia Knowledge = Semantics-Aware Recommender Systems / Basile, Pierpaolo; Musto, Cataldo; de Gemmis, Marco; Lops, Pasquale; Narducci, Fedelucio; Semeraro, Giovanni. - STAMPA. - 475:(2014), pp. 163-169. [10.1007/978-3-319-12024-9_21]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/215920
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