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Azzurra Ragone – One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.

  • SAC – Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing – SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.

Roberto Mirizzi – One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.

  • SAC – Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing – SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.

Eugenio Di Sciascio – One of the best experts on this subject based on the ideXlab platform.

  • web 3 0 in Action Vector space model for semantic movie recommendations
    ACM Symposium on Applied Computing, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.

  • SAC – Web 3.0 in Action: Vector Space Model for semantic (movie) Recommendations
    Proceedings of the 27th Annual ACM Symposium on Applied Computing – SAC '12, 2012
    Co-Authors: Roberto Mirizzi, Tommaso Di Noia, Eugenio Di Sciascio, Azzurra Ragone
    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.