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    block this user Luisa Mich

    Associate Professor / luisa.mich@unitn.it

    University of Trento

    Information and Communication Technologies in Tourism 2007

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    This paper presents the model of an Intelligent Visual Travel Recommender system (IV-TRS). IV-TRS is a system that makes use of visual information such as images and video, along with audio, to present a virtual tour of the destination. The IV-TRS model is divided into three layers, namely Augmented Web, Application and Intelligence layers. The IV-TRS makes use of the TRIPS (Tour Recommendation using Image-Based Planning with SCORM) concept to bring together relevant tourism data stored in the form of Sharable Content Objects (SCOs). The Augmented Web Layer consists of data stored as SCOs built on the SCORM standard with their location is resolved by CORDRA. SCORM (Shamble Content Object Reference Model) is a model that defines how information is to be stored, referenced and used by packaging data with metadata. CORDRA (Content Object Repository Discovery and Registration/Resolution Architecture) is a model that aims to locate and reference SCORM repositories over the web. The Intelligence Layer provides the reasoning engine of the system, and can be built on Semantic Web technology, Expert Systems technology, or Artificial Neural Networks. For this research, we focus on the Semantic Web technology for building the Intelligence Layer. The Application layer is made up of two components; namely, Search and Presentation. The Search component makes use of the CORDRA functionality to crawl and index the SCOs on the Internet. The Presentation component aims to provide a user interface that lets the user customize the presentation based on personal preferences. The IV-TRS system will provide the user a virtual experience to support better destination selection, and tour planning.

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    Description

    Title : Information and Communication Technologies in Tourism 2007
    Author(s) : Marianna Sigala, Luisa Mich, Jamie Murphy, Nadzeya Kiyavitskaya, Nicola Zeni, James R Cordy, John Mylopoulos
    Abstract : This paper presents the model of an Intelligent Visual Travel Recommender system (IV-TRS). IV-TRS is a system that makes use of visual information such as images and video, along with audio, to present a virtual tour of the destination. The IV-TRS model is divided into three layers, namely Augmented Web, Application and Intelligence layers. The IV-TRS makes use of the TRIPS (Tour Recommendation using Image-Based Planning with SCORM) concept to bring together relevant tourism data stored in the form of Sharable Content Objects (SCOs). The Augmented Web Layer consists of data stored as SCOs built on the SCORM standard with their location is resolved by CORDRA. SCORM (Shamble Content Object Reference Model) is a model that defines how information is to be stored, referenced and used by packaging data with metadata. CORDRA (Content Object Repository Discovery and Registration/Resolution Architecture) is a model that aims to locate and reference SCORM repositories over the web. The Intelligence Layer provides the reasoning engine of the system, and can be built on Semantic Web technology, Expert Systems technology, or Artificial Neural Networks. For this research, we focus on the Semantic Web technology for building the Intelligence Layer. The Application layer is made up of two components; namely, Search and Presentation. The Search component makes use of the CORDRA functionality to crawl and index the SCOs on the Internet. The Presentation component aims to provide a user interface that lets the user customize the presentation based on personal preferences. The IV-TRS system will provide the user a virtual experience to support better destination selection, and tour planning.
    Keywords : economics, management science

    Subject : unspecified
    Area : Other
    Year : 2007

    Affiliations University of Trento
    Editors : Marianna Sigala, Luisa Mich, Jamie Murphy
    Publisher : Springer Vienna
    City : Vienna
    Pages : 413-424-424
    Url : http://www.springerlink.com/index/10.1007/978-3-211-69566-1
    Isbn : 978-3-211-69564-7
    Doi : 10.1007/978-3-211-69566-1

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