Mapping of Tourism Destinations to Travel Behavioural Patterns

Authors: 
Mete Sertkan
Julia Neidhardt
Hannes Werthner
Type: 
Speech with proceedings
Proceedings: 
Information and Communication Technologies in Tourism 2018
Publisher: 
Springer, Cham
Pages: 
422 - 434
Year: 
2018
ISBN: 
ISBN: 978-3-319-72922-0
Abstract: 
Tourism is an information intensive domain, where recommender systems have become an essential tool to guide customers to the right products. However, they are facing major challenges, since tourism products are considered as complex and emotional. It has been shown that the seven-factor model is a legitimate way to counter some of these challenges. However, in order to recommend an item, it has also to be described in terms of this model. This work´s aim is to find a scalable way to map tourism destinations, defined by their attributes, to the seven-factor model. Through statistical analysis and learning methods it is shown that there is a significant relationship between particular destination features and the seven-factors and that destinations can be grouped in a meaningful way using their attributes.
TU Focus: 
Information and Communication Technology
Reference: 

M. Sertkan, J. Neidhardt, H. Werthner:
"Mapping of Tourism Destinations to Travel Behavioural Patterns";
Vortrag: ENTER Conference 2018, Jönköping, Sweden; 24.01.2018 - 26.01.2018; in: "Information and Communication Technologies in Tourism 2018", Springer, Cham, (2018), ISBN: 978-3-319-72922-0; S. 422 - 434.

Zusätzliche Informationen

Last changed: 
11.01.2018 13:20:28
Accepted: 
Accepted
TU Id: 
265148
Invited: 
Department Focus: 
Business Informatics
Author List: 
M. Sertkan, J. Neidhardt, H. Werthner
Abstract German: 
Tourism is an information intensive domain, where recommender systems have become an essential tool to guide customers to the right products. However, they are facing major challenges, since tourism products are considered as complex and emotional. It has been shown that the seven-factor model is a legitimate way to counter some of these challenges. However, in order to recommend an item, it has also to be described in terms of this model. This work´s aim is to find a scalable way to map tourism destinations, defined by their attributes, to the seven-factor model. Through statistical analysis and learning methods it is shown that there is a significant relationship between particular destination features and the seven-factors and that destinations can be grouped in a meaningful way using their attributes.