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

Andrea Generosi (), José Yuri Villafan (), Maddalena Ferretti () and Maura Mengoni ()
Additional contact information
Andrea Generosi: Pegaso University
José Yuri Villafan: Marche Polytechnic University
Maddalena Ferretti: Marche Polytechnic University
Maura Mengoni: Marche Polytechnic University

Information Technology & Tourism, 2025, vol. 27, issue 3, No 11, 797-831

Abstract: 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.

Keywords: Recommender systems; Branding; Machine learning for tourism; Collaborative web platform (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40558-025-00327-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infott:v:27:y:2025:i:3:d:10.1007_s40558-025-00327-1

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/40558

DOI: 10.1007/s40558-025-00327-1

Access Statistics for this article

Information Technology & Tourism is currently edited by Zheng Xiang

More articles in Information Technology & Tourism from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().