The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.

Linked Open Data to support Content-based Recommender Systems / Di Noia, Tommaso; Mirizzi, Roberto; Ostuni, Vito Claudio; Romito, Davide; Zanker, Markus. - ELETTRONICO. - (2012), pp. 1-8. (Intervento presentato al convegno 8th International Conference on Semantic Systems, I-SEMANTICS '12 tenutosi a Graz, Austria nel September 5-7, 2012) [10.1145/2362499.2362501].

Linked Open Data to support Content-based Recommender Systems

Tommaso Di Noia;Roberto Mirizzi;Vito Claudio Ostuni;
2012-01-01

Abstract

The World Wide Web is moving from a Web of hyper-linked Documents to a Web of linked Data. Thanks to the Semantic Web spread and to the more recent Linked Open Data (LOD) initiative, a vast amount of RDF data have been published in freely accessible datasets. These datasets are connected with each other to form the so called Linked Open Data cloud. As of today, there are tons of RDF data available in the Web of Data, but only few applications really exploit their potential power. In this paper we show how these data can successfully be used to develop a recommender system (RS) that relies exclusively on the information encoded in the Web of Data. We implemented a content-based RS that leverages the data available within Linked Open Data datasets (in particular DBpedia, Freebase and LinkedMDB) in order to recommend movies to the end users. We extensively evaluated the approach and validated the effectiveness of the algorithms by experimentally measuring their accuracy with precision and recall metrics.
2012
8th International Conference on Semantic Systems, I-SEMANTICS '12
978-1-4503-1112-0
Linked Open Data to support Content-based Recommender Systems / Di Noia, Tommaso; Mirizzi, Roberto; Ostuni, Vito Claudio; Romito, Davide; Zanker, Markus. - ELETTRONICO. - (2012), pp. 1-8. (Intervento presentato al convegno 8th International Conference on Semantic Systems, I-SEMANTICS '12 tenutosi a Graz, Austria nel September 5-7, 2012) [10.1145/2362499.2362501].
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/11589/52472
Citazioni
  • Scopus 231
  • ???jsp.display-item.citation.isi??? ND
social impact