This paper provides an overview of the work done in theLinked Open Data-enabled Recommender Systems challenge, in whichwe proposed an ensemble of algorithms based on popularity, Vector SpaceModel, Random Forests, Logistic Regression, and PageRank, running ona diverse set of semantic features. We ranked 1st in the top-N recom-mendation task, and 3rd in the tasks of rating prediciton and diversity

Aggregation Strategies for Linked Open Data-enabled Recommender Systems / Basile, Pierpaolo; Musto, Cataldo; de Gemmis, Marco; Lops, Pasquale; Narducci, Fedelucio; Semeraro, Giovanni. - ELETTRONICO. - (2014). (Intervento presentato al convegno ESWC 2014 tenutosi a Crete, Greece nel May 25-29, 2014).

Aggregation Strategies for Linked Open Data-enabled Recommender Systems

Fedelucio Narducci;
2014-01-01

Abstract

This paper provides an overview of the work done in theLinked Open Data-enabled Recommender Systems challenge, in whichwe proposed an ensemble of algorithms based on popularity, Vector SpaceModel, Random Forests, Logistic Regression, and PageRank, running ona diverse set of semantic features. We ranked 1st in the top-N recom-mendation task, and 3rd in the tasks of rating prediciton and diversity
2014
ESWC 2014
Aggregation Strategies for Linked Open Data-enabled Recommender Systems / Basile, Pierpaolo; Musto, Cataldo; de Gemmis, Marco; Lops, Pasquale; Narducci, Fedelucio; Semeraro, Giovanni. - ELETTRONICO. - (2014). (Intervento presentato al convegno ESWC 2014 tenutosi a Crete, Greece nel May 25-29, 2014).
File in questo prodotto:
Non ci sono file associati a questo prodotto.

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11589/215945
Citazioni
  • Scopus ND
  • ???jsp.display-item.citation.isi??? ND
social impact