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 profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. MORE is freely available as a Facebook application.
Web 3.0 in Action: Vector Space Model for Semantic (Movie) Recommendations / Mirizzi, Roberto; Di Noia, Tommaso; Di Sciascio, Eugenio; Ragone, Azzurra. - ELETTRONICO. - (2012), pp. 403-405. (Intervento presentato al convegno 27th Annual ACM Symposium on Applied Computing, SAC 2012 tenutosi a Trento, Italy nel March 26-30, 2012) [10.1145/2245276.2245354].
Web 3.0 in Action: Vector Space Model for Semantic (Movie) Recommendations
Roberto Mirizzi;Tommaso Di Noia;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 profile. MORE exploits the power of social knowledge bases (e.g. DBpedia) to detect semantic similarities among movies. These similarities are computed by a Semantic version of the classical Vector Space Model (sVSM), applied to semantic datasets. 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.
