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Recommender Systems in Tourism

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Handbook of e-Tourism

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.

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Author information

Authors and Affiliations

  1. Faculty of Computer Science, Free University of Bozen-Bolzano, Bozen-Bolzano, Italy

    Francesco Ricci

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  1. Francesco Ricci
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Correspondence to Francesco Ricci .

Editor information

Editors and Affiliations

  1. The Howard Feiertag Department of Hospitality and Tourism Management, Pamplin College of Business, Virginia Tech, Blacksburg, VA, USA

    Zheng Xiang

  2. Department of Economics, Geography, Law and Tourism, The European Tourism Research Institute, Mid-Sweden University, Östersund, Jämtland, Sweden

    Matthias Fuchs

  3. Annenberg School for Communication and Journalism, University of Southern California, Los Angeles, CA, USA

    Ulrike Gretzel

  4. Institute for Digital Transformation, Ravensburg-Weingarten University of Applied Sciences, Weingarten, Germany

    Wolfram Höpken

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Ricci, F. (2022). Recommender Systems in Tourism. In: Xiang, Z., Fuchs, M., Gretzel, U., Höpken, W. (eds) Handbook of e-Tourism. Springer, Cham. https://doi.org/10.1007/978-3-030-48652-5_26

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