DIGITALIMM
Scheda del progetto
Università degli Studi di Torino
Prof.ssa Nadia Campaniello
Prof. Pietro Garibaldi
Museums currently adopt management choices using a qualitative approach. The objective of the project is to support them by using a quantitative approach using big data and machine learning techniques. The start-up we aim to create will use the algorithms we are developing to provide museums with a management model that uses a multidisciplinary, data-driven, exportable and scalable approach. The spin-off activities will concern, by way of example, the estimation of the demand and value of the museum asset, the implementation of predictive models of visitor flows and the dynamic pricing of entrances based on artificial intelligence.
Contacts:
Ref.
Prof. Giovanni Mastrobuoni
To make the museum visit experience more personalized and engaging, an application (IMuse) will be developed which will allow us to acquire information on visitor preferences so as to be able to suggest museum visits and exhibitions. Furthermore, using artificial intelligence algorithms, we will create connections between works exhibited in different museums that visitors can explore by scanning QR codes and using the I-Muse App. The aim is to increase the number of visitors, especially in the younger age group who seem to be the ones absent from museum visits.
The I-Muse application will support museum management policies and transform the visitor experience in enjoying the cultural products offered by Turin's museums. By scanning QR codes, visitors will reveal their preferences to the application. The information collected will be processed with machine learning techniques and based on data analysis, specific paths will be recommended to the individual user of the works of art. The personalized advice given to the individual visitor will stimulate the use of a greater number of cultural works that would otherwise have remained unknown, also increasing the degree of interaction with them. The paths will be suggested by algorithms that will analyze the data with machine and statistical learning techniques, developed by researchers at the Polytechnic of Turin. The personalization of museum visits will lead to an increase in attendance numbers and in the quality of the visitor experience.
The expected result consists in the increase in the number of visitors in the museum structures in the Turin area and in the museums that will join the project. This will be made possible through the implementation of new management methods, supported by advanced digitalisation techniques which will favor the optimization of activities. Particular attention will be paid to the involvement of younger audiences.