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.
| Original language | English |
|---|---|
| Pages (from-to) | 61-82 |
| Number of pages | 22 |
| Journal | Information Technology & Tourism |
| Volume | 19 |
| Issue number | 1-4 |
| DOIs | |
| Publication status | Published - 1 Jun 2018 |
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Keywords
- City trip
- Latent class model
- Stated choice experiment
- Travel recommender systems
- User model
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Estimating a latent-class user model for travel recommender systems. / Arentze, Theo; Kemperman, Astrid; Aksenov, Petr.
In: Information Technology & Tourism, Vol. 19, No. 1-4, 01.06.2018, p. 61-82.Research output: Contribution to journal › Article › Academic › peer-review
TY - JOUR
T1 - Estimating a latent-class user model for travel recommender systems
AU - Arentze, Theo
AU - Kemperman, Astrid
AU - Aksenov, Petr
PY - 2018/6/1
Y1 - 2018/6/1
N2 - 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.
AB - 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.
KW - City trip
KW - Latent class model
KW - Stated choice experiment
KW - Travel recommender systems
KW - User model
UR - http://www.scopus.com/inward/record.url?scp=85048079291&partnerID=8YFLogxK
U2 - 10.1007/s40558-018-0105-z
DO - 10.1007/s40558-018-0105-z
M3 - Article
AN - SCOPUS:85048079291
VL - 19
SP - 61
EP - 82
JO - Information Technology & Tourism
JF - Information Technology & Tourism
SN - 1098-3058
IS - 1-4
ER -