IDEAS home Printed from https://ideas.repec.org/h/spr/prbchp/978-3-031-81962-9_65.html
   My bibliography  Save this book chapter

Toward a More Authentic Travel Experience: Exploring the Potential of AI-Powered Recommendations for Unusual Experiences

Author

Listed:
  • Idalia Maldonado Castillo

    (Escuela Superior de Cómputo-Instituto Politécnico Nacional)

  • Jorge Ferrer Tenorio

    (Escuela Superior de Cómputo-Instituto Politécnico Nacional)

Abstract

Travelers conduct online research before going on a trip, using social media, travel apps, and websites, Artificial Intelligence (AI) has the potential to provide tailored experiences and provide the best and more unusual experiences. This study aims to improve the travel experience by using an innovative mobile application that provides cool, hidden, and unusual things to do in Mexico City with personalized recommendations. By employing artificial intelligence techniques and machine learning models, the application will identify and recommend tourist destinations that align with the traveler's individual interests. The primary objective is to provide travelers seeking a more authentic and original travel experience, with recommendations guiding users toward less frequented and unusual attractions in Mexico City. Implications about what authentic experiences are will be discussed. The application will provide a conversational agent capable of responding queries, facilitating user interaction, and providing information about the recommended sites.

Suggested Citation

  • Idalia Maldonado Castillo & Jorge Ferrer Tenorio, 2025. "Toward a More Authentic Travel Experience: Exploring the Potential of AI-Powered Recommendations for Unusual Experiences," Springer Proceedings in Business and Economics,, Springer.
    • Handle: RePEc:spr:prbchp:978-3-031-81962-9_65
    DOI: 10.1007/978-3-031-81962-9_65
    as

    Download full text from publisher

    To our knowledge, this item is not available for download. To find whether it is available, there are three options:
    1. Check below whether another version of this item is available online.
    2. Check on the provider's web page whether it is in fact available.
    3. Perform a
    for a similarly titled item that would be available.

    More about this item

    Keywords

    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:prbchp:978-3-031-81962-9_65. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.