In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her prole. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application.
Movie Recommendation with DBpedia / Mirizzi, Roberto; Di Noia, Tommaso; Ragone, Azzurra; Ostuni, Vito Claudio; Di Sciascio, Eugenio. - ELETTRONICO. - (2012), pp. 101-112. (Intervento presentato al convegno 3rd Italian Information Retrieval Workshop, IIR 2012 tenutosi a Bari, Italy nel January 26-27, 2012).
Movie Recommendation with DBpedia
Roberto Mirizzi;Tommaso Di Noia;Azzurra Ragone;Vito Claudio Ostuni;Eugenio Di Sciascio
2012-01-01
Abstract
In this paper we present MORE (acronym of MORE than MOvie REcommendation), a Facebook application that semantically recommends movies to the user leveraging the knowledge within Linked Data and the information elicited from her prole. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic sim- ilarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. Precision and recall experiments prove the validity of our ap- proach for movie recommendation. MORE is freely available as a Facebook application.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
