Although ubiquitous and fast access to the Internet allows us to admire objects and artworks exhibited worldwide from the comfort of our home, visiting a museum or an exhibition remains an essential experience today. Current technologies can help make that experience even more satisfying. For instance, they can assist the user during the visit, personalizing her experience by suggesting the artworks of her higher interest and providing her with related textual and multimedia content. To this aim, it is necessary to automatically acquire information relating to the active user. In this paper, we show how a deep neural network-based approach can allow us to obtain accurate information for understanding the behavior of the visitor alone or in a group. This information can also be used to identify users similar to the active one to suggest not only personalized itineraries but also possible visiting companions for promoting the museum as a vehicle for social and cultural inclusion.
Ferrato, A., Limongelli, C., Mezzini, M., & Sansonetti, G. (2022). A Deep Learning-based Approach to Model Museum Visitors. In CEUR Workshop Proceedings (pp.217-221). CEUR-WS.
|Titolo:||A Deep Learning-based Approach to Model Museum Visitors|
|Data di pubblicazione:||2022|
|Citazione:||Ferrato, A., Limongelli, C., Mezzini, M., & Sansonetti, G. (2022). A Deep Learning-based Approach to Model Museum Visitors. In CEUR Workshop Proceedings (pp.217-221). CEUR-WS.|
|Appare nelle tipologie:||4.1 Contributo in Atti di convegno|