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Recommender Systems in Tourism

In: Handbook of e-Tourism

Author

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  • Francesco Ricci

    (Free University of Bozen-Bolzano)

Abstract

Recommender systems (RSs) are information search and filtering tools that provide suggestions for items to be of use to a user. They are now common in many Internet applications (Google News, Amazon, TripAdvisor), helping users to make better choices while searching for news, books, or vacations. RSs exploit data mining and information retrieval techniques to predict to what extent an item fits the user needs and wants. RSs interact with the user to fine-tune these suggestions while presenting a selection of the items, among those having the largest predicted fit score. RSs have been used in tourism applications for suggesting points of interest to visit, holiday properties, and flights, or even generating complete plans for holidays, that is, bundling different types of more elementary items (e.g., accommodations and events) in one recommendation bundle. In this chapter, we will first introduce basic recommender systems principles and techniques. We will discuss the general functioning of a recommender system and how various techniques are used to implement the model components. We will then present important key dimensions for recommender systems especially considering the travel and tourism application scenario. We will close this chapter by discussing some limitations and open challenges for recommender systems research.

Suggested Citation

  • Francesco Ricci, 2022. "Recommender Systems in Tourism," Springer Books, in: Zheng Xiang & Matthias Fuchs & Ulrike Gretzel & Wolfram Höpken (ed.), Handbook of e-Tourism, chapter 19, pages 457-474, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-48652-5_26
    DOI: 10.1007/978-3-030-48652-5_26
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    Cited by:

    1. Federica Cena & Noemi Mauro & Amon Rapp, 2023. "How do sensory features of places impact on spatial exploration of people with autism? A user study," Information Technology & Tourism, Springer, vol. 25(1), pages 105-132, March.

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