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

Author

Listed:
  • Andrea Generosi

    (Pegaso University)

  • José Yuri Villafan

    (Marche Polytechnic University)

  • Maddalena Ferretti

    (Marche Polytechnic University)

  • Maura Mengoni

    (Marche Polytechnic University)

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.

Suggested Citation

  • Andrea Generosi & José Yuri Villafan & Maddalena Ferretti & Maura Mengoni, 2025. "A recommender-based web platform to boost tourism in marginal territories," Information Technology & Tourism, Springer, vol. 27(3), pages 797-831, September.
    • Handle: RePEc:spr:infott:v:27:y:2025:i:3:d:10.1007_s40558-025-00327-1
    DOI: 10.1007/s40558-025-00327-1
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    References listed on IDEAS

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    1. Ko Koens & Albert Postma & Bernadett Papp, 2018. "Is Overtourism Overused? Understanding the Impact of Tourism in a City Context," Sustainability, MDPI, vol. 10(12), pages 1-15, November.
    2. Yong Eui Kim & Sang-Min Choi & Dongwoo Lee & Yeong Geon Seo & Suwon Lee, 2023. "A Reliable Prediction Algorithm Based on Genre2Vec for Item-Side Cold-Start Problems in Recommender Systems with Smart Contracts," Mathematics, MDPI, vol. 11(13), pages 1-25, July.
    3. Zehui Wang & Wolfram Höpken & Dietmar Jannach, 2025. "A survey on point-of-interest recommendations leveraging heterogeneous data," Information Technology & Tourism, Springer, vol. 27(1), pages 29-73, March.
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