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. © 2012 Authors.
Web 3.0 in action: Vector Space Model for semantic (movie) Recommendations
Di Noia T.;Di Sciascio E.;Ragone Azzurra
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. © 2012 Authors.File in questo prodotto:
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