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Estimating a latent-class user model for travel recommender systems

Author

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  • Theo Arentze

    (Eindhoven University of Technology)

  • Astrid Kemperman

    (Eindhoven University of Technology)

  • Petr Aksenov

    (Eindhoven University of Technology)

Abstract

In determining the selection of sites to visit on a trip tourists have to trade-off attraction values against routing and time-use characteristics of points of interest (POIs). For recommending optimal personalized travel plans an accurate assessment of how users make these trade-offs is important. In this paper we report the results of a study conducted to estimate a user model for travel recommender systems. The proposed model is part of c-Space—a tour-recommender system for tourists on a city trip which uses the LATUS algorithm to find personalized optimal tours. The model takes into account a multi-attribute utility function of POIs as well as dynamic needs of persons on a trip. A stated choice experiment is designed where the current need is manipulated as a context variable and activity choice alternatives are varied. A random sample of 316 individuals participated in the on-line survey. A latent-class analysis shows that significant differences exist between tourists in terms of how they make the trade-offs between the factors and respond to needs. The estimation results provide the parameters of a multi-class user model that can be used for travel recommender systems.

Suggested Citation

  • Theo Arentze & Astrid Kemperman & Petr Aksenov, 2018. "Estimating a latent-class user model for travel recommender systems," Information Technology & Tourism, Springer, vol. 19(1), pages 61-82, June.
    • Handle: RePEc:spr:infott:v:19:y:2018:i:1:d:10.1007_s40558-018-0105-z
    DOI: 10.1007/s40558-018-0105-z
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    References listed on IDEAS

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    Cited by:

    1. Kemperman, Astrid, 2021. "A review of research into discrete choice experiments in tourism: Launching the Annals of Tourism Research Curated Collection on Discrete Choice Experiments in Tourism," Annals of Tourism Research, Elsevier, vol. 87(C).

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