Estimating a latent-class user model for travel recommender systems

Theo Arentze (), Astrid Kemperman and Petr Aksenov
Additional contact information
Theo Arentze: Eindhoven University of Technology
Astrid Kemperman: Eindhoven University of Technology
Petr Aksenov: Eindhoven University of Technology

Information Technology & Tourism, 2018, vol. 19, issue 1, No 3, 82 pages

Abstract: 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.

Keywords: Travel recommender systems; User model; City trip; Stated choice experiment; Latent class model (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s40558-018-0105-z Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:infott:v:19:y:2018:i:1:d:10.1007_s40558-018-0105-z

Ordering information: This journal article can be ordered from
http://www.springer. ... ystems/journal/40558

DOI: 10.1007/s40558-018-0105-z

Access Statistics for this article

Information Technology & Tourism is currently edited by Zheng Xiang

More articles in Information Technology & Tourism from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().