Nowadays, pervasive digital technologies, innovative tools, and fruition strategies are generally adopted in the Cultural Heritage (CH) field to improve the quality of services and to enhance visitor experiences. To these aims, expert systems based on learning approaches are specifically designed. In this paper we describe a machine learning approach, implemented in a software tool, that can be usefully used to assist and tov support visitors in cultural spaces. The deployed system architecture is deeply analysed and, finally, a study in CH context is reported.

Visitor assistant tools based on machine learning approaches in cultural heritage contexts / Cuomo, Salvatore; Chirico, Ugo. - 2018-:(2018), pp. 485-489. (Intervento presentato al convegno 13th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2017 tenutosi a ind nel 2017) [10.1109/SITIS.2017.85].

Visitor assistant tools based on machine learning approaches in cultural heritage contexts

Cuomo, Salvatore
;
2018

Abstract

Nowadays, pervasive digital technologies, innovative tools, and fruition strategies are generally adopted in the Cultural Heritage (CH) field to improve the quality of services and to enhance visitor experiences. To these aims, expert systems based on learning approaches are specifically designed. In this paper we describe a machine learning approach, implemented in a software tool, that can be usefully used to assist and tov support visitors in cultural spaces. The deployed system architecture is deeply analysed and, finally, a study in CH context is reported.
2018
9781538642832
Visitor assistant tools based on machine learning approaches in cultural heritage contexts / Cuomo, Salvatore; Chirico, Ugo. - 2018-:(2018), pp. 485-489. (Intervento presentato al convegno 13th International Conference on Signal-Image Technology and Internet-Based Systems, SITIS 2017 tenutosi a ind nel 2017) [10.1109/SITIS.2017.85].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11588/728298
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