Recommender engines are software applications employed in online tourism information searches to suggest useful content and guide user choices. They represent a significant area of research within the tourism sector, crucially influencing traveler decisions through personalized recommendations. This paper focuses specifically on the Branding4Resilience (B4R) project, whose objective is to promote inner areas, by co-designing virtuous transformations with their inhabitants through novel branding strategies and digital enabling infrastructures. All experiments and assessments described in this study were conducted within the italian area of Sassoferrato, corresponding to the criteria defined by the B4R initiative. The paper introduces a dedicated web platform aimed at enhancing tourism by directing attention towards inland destinations and networking opportunities. The platform enables local providers of tourism-related services, such as guides, event organizers, and restaurateurs, to connect effectively with tourists seeking distinctive experiences. By facilitating interactions, the platform promotes collaboration, innovation, and the co-creation of personalized experiences, transforming tourists into active participants and contributors. To evaluate user experience, perceived usefulness, and innovation of the platform, we analyzed user responses to a structured questionnaire, which combined frequently addressed questions from existing literature with newly developed items specific to our platform. Constructs included tourist platform usage, perceived utility, content quality, design and usability, and innovative elements. The implemented algorithm demonstrated an average sensitivity of 79.74%, highlighting its efficacy in providing relevant suggestions to the platform’s “consumers” users.

A recommender-based web platform to boost tourism in marginal territories / Generosi, A.; Villafan, J. Y.; Ferretti, M.; Mengoni, M.. - In: INFORMATION TECHNOLOGY & TOURISM. - ISSN 1098-3058. - ELETTRONICO. - 27:3(2025), pp. 797-831. [10.1007/s40558-025-00327-1]

A recommender-based web platform to boost tourism in marginal territories

Generosi A.
;
Villafan J. Y.
;
Ferretti M.;Mengoni M.
2025-01-01

Abstract

Recommender engines are software applications employed in online tourism information searches to suggest useful content and guide user choices. They represent a significant area of research within the tourism sector, crucially influencing traveler decisions through personalized recommendations. This paper focuses specifically on the Branding4Resilience (B4R) project, whose objective is to promote inner areas, by co-designing virtuous transformations with their inhabitants through novel branding strategies and digital enabling infrastructures. All experiments and assessments described in this study were conducted within the italian area of Sassoferrato, corresponding to the criteria defined by the B4R initiative. The paper introduces a dedicated web platform aimed at enhancing tourism by directing attention towards inland destinations and networking opportunities. The platform enables local providers of tourism-related services, such as guides, event organizers, and restaurateurs, to connect effectively with tourists seeking distinctive experiences. By facilitating interactions, the platform promotes collaboration, innovation, and the co-creation of personalized experiences, transforming tourists into active participants and contributors. To evaluate user experience, perceived usefulness, and innovation of the platform, we analyzed user responses to a structured questionnaire, which combined frequently addressed questions from existing literature with newly developed items specific to our platform. Constructs included tourist platform usage, perceived utility, content quality, design and usability, and innovative elements. The implemented algorithm demonstrated an average sensitivity of 79.74%, highlighting its efficacy in providing relevant suggestions to the platform’s “consumers” users.
2025
Branding; Collaborative web platform; Machine learning for tourism; Recommender systems
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/348693
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