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 (COMMUNICATIONS IN COMPUTER AND INFORMATION SCIENCE). - In: Semantic Web Evaluation Challenge SemWebEval 2014 at ESWC 2014, Anissaras, Crete, Greece, May 25-29, 2014, Revised Selected Papers / [a cura di] Valentina Presutti, Milan Stankovic, Erik CambriaIván Cantador, Angelo Di Iorio, Tommaso Di Noia, Christoph Lange, Diego Reforgiato Recupero, Anna Tordai. - STAMPA. - Cham, CH : Springer, 2014. - ISBN 978-3-319-12023-2. - 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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
