Analysing visitors’ behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors’ behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users’ experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users’ stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).

Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis / Ceccarelli, Sofia; Cesta, Amedeo; Cortellessa, Gabriella; De Benedictis, Riccardo; Fracasso, Francesca; Leopardi, Laura; Ligios, Luca; Lombardi, Ernesto; Malatesta, Saverio Giulio; Oddi, Angelo; Pagano, Alfonsina; Palombini, Augusto; Romagna, Gianmauro; Sanzari, Marta; Schaerf, Marco. - In: DIGITAL APPLICATIONS IN ARCHAEOLOGY AND CULTURAL HERITAGE. - ISSN 2212-0548. - 33:(2024). [10.1016/j.daach.2024.e00340]

Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis

Cesta, Amedeo;Cortellessa, Gabriella;Fracasso, Francesca;Leopardi, Laura;Malatesta, Saverio Giulio;Oddi, Angelo;Palombini, Augusto;Romagna, Gianmauro;Sanzari, Marta;Schaerf, Marco
2024

Abstract

Analysing visitors’ behaviour in a museum or in a cultural site is a crucial element to manage spaces and artworks arrangement as well as improving the visit experience. This paper presents the preliminary results of the ARTEMISIA project, exploiting Artificial Intelligence (AI) techniques to study, design and develop a methodology to interpret visitors’ behaviour within a museum context, namely the Museum of Rome in Palazzo Braschi (Rome, Italy). The aim is to combine literature on users’ experience (UX) analysis with experimental data coming from the visitor anonymous tracking out of motion sensors (users’ stand-still positions, viewpoint direction, movements), merging approaches of different research domains. Through the use of agglomerative hierarchical clustering algorithms, four categories of visitors were identified, then associated to user profiles emerged by UX evaluations. Such analysis may lead to new forms of visitors profiling and to the development of a new generation of customised applications in public and private contexts. Identifying and predicting users’ patterns with respect to museum halls arrangement may also be useful to suggest improvement in the museum spaces and exhibitions (new indications, updated storytelling or changes in thematic configuration).
2024
museum studies; user experience evaluation; artificial intelligence; museum visit trajectories; visitors’ segmentation
01 Pubblicazione su rivista::01a Articolo in rivista
Evaluating visitors’ experience in museum: Comparing artificial intelligence and multi-partitioned analysis / Ceccarelli, Sofia; Cesta, Amedeo; Cortellessa, Gabriella; De Benedictis, Riccardo; Fracasso, Francesca; Leopardi, Laura; Ligios, Luca; Lombardi, Ernesto; Malatesta, Saverio Giulio; Oddi, Angelo; Pagano, Alfonsina; Palombini, Augusto; Romagna, Gianmauro; Sanzari, Marta; Schaerf, Marco. - In: DIGITAL APPLICATIONS IN ARCHAEOLOGY AND CULTURAL HERITAGE. - ISSN 2212-0548. - 33:(2024). [10.1016/j.daach.2024.e00340]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1709924
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