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Mapping of Tourism Destinations to Travel Behavioural Patterns
1. ENTER 2018 Research Track Slide Number 1
Mapping of Tourism Destinations to
Travel Behavioural Patterns
Mete Sertkan, Julia Neidhardt, and Hannes Werthner
E-Commerce Group, TU Wien, Austria
mete.sertkan@ec.tuwien.ac.at
http://ec.tuwien.ac.at
2. ENTER 2018 Research Track Slide Number 2
Outline
1. Introduction
2. State of the Art
3. The Data
4. Model building
5. Conclusions
6. Future Work
4. ENTER 2018 Research Track Slide Number 4
Background
• 65% travellers start researching online
• Difficulties in expressing preferences/needs
• Recommender Systems (RSs) are facilitating the
decision making
• Personality based RSs
– Five Factor Model (Big Five)
– 17 Tourist Roles (Gibson & Yiannakis, 2002)
– Seven-Factor Model (Neidhardt et al., 2014,2015)
5. ENTER 2018 Research Track Slide Number 5
Sun & Chill-Out
Knowledge & Travel
Independence & History
Culture & IndulgenceSocial & Sports
Action & Fun
Nature & Recreation
1
0
0.5
6. ENTER 2018 Research Track Slide Number 6
Determining the Seven-Factors
• For users: picture based approach
• For tourism products: manually
– Tourism products are complex
– Experts mapped >10K manually
Seven-Factors
7. ENTER 2018 Research Track Slide Number 7
Key Idea
Dahab, Egypt
nightlife: 0.49
wellness: 0.79
…
sea: 1
forest: 0
beach: 1
8. ENTER 2018 Research Track Slide Number 8
State of the Art
• Eliciting the users' unknown preferences (Neidhardt et al.,
2014)
• A picture-based approach to recommender systems (Neidhardt
et al., 2015)
• Use of Travel Personalities in Destination Recommendation
Systems (Gretzel et al., 2004)
• Recommender Systems: Introduction and Challenges (Ricci et
al., 2015)
• Personality and recommender systems (Tkalcic et al., 2015)
• STS: A Context-Aware Mobile Recommender System for Places
of Interest (Braunhofer et al., 2014)
10. ENTER 2018 Research Track Slide Number 10
Data Source
• German start-up
• SQL dump with 30K destinations
• Majority in USA, Germany, France, Italy, Spain,
Great Britain, Austria, Greece, Switzerland, and
Sweden (65%)
• Between small hamlet (population < 100) and
metropolis (population > 1M)
11. ENTER 2018 Research Track Slide Number 11
Destination Attributes
• Motivational ratings
– [0,1], degree of appropriation
– 27 ratings, such as nightlife, wellness, shopping, nature
& landscape, image & flair, culture, sightseeing, etc.
– Considering infrastructure, climate, user opinions,
number of services, and image & branding
• Geographical attributes
– 0|1, sign of presence/absence
– 14 attributes such as sea, mountain, lake, island, sandy
beach, metropolis, forest, river, etc.
12. ENTER 2018 Research Track Slide Number 12
Example Entry
id city country nightlife wellness … sea forest beach
2 Dahab Egypt 0.43 0.79 … 1 0 1
id & location motivational
ratings
geographical
attributes
13. ENTER 2018 Research Track Slide Number 13
Data Sample
561 destinations, mapped by 3 experts from an
Austrian eTourism company
id city country F1 F2 F3 F4 F5 F6 F7
2 Dahab Egypt 1 0.25 0.50 0.50 0.75 0.50 0.75
F1: Sun & Chill-Out
F2: Knowledge & Travel
F3: Independence & History
F4: Culture & Indulgence
F5: Social & Sports
F6: Action & Fun
F7: Nature & Recreation
14. ENTER 2018 Research Track Slide Number 14
Cluster analysis
• Partitioning Around Medoids (PAM)
• Medoid = actual data point = most
representative point of the cluster
• Gower Distance
• Silhouette width, assess cluster size
6 Cluster Solution
15. ENTER 2018 Research Track Slide Number 15
Cluster 1 – 3
Brussels (BE)
metropole
sightseeing
culture
nightlife
entertainment
C2 (N=47)
Liederbach (DE)
small suburb
no tourism
C1 (N=184)
Schönberg am Kamp (AT)
in the nature
silent
peaceful
hiking & cycling
C3 (N=141)
16. ENTER 2018 Research Track Slide Number 16
Cluster 4 – 6
Grand Baie (MU)
sea
beach
entertainment
nightlife
sailing & diving
C5 (N=60)
Todtnau (DE)
in the nature
hiking & cycling
entertainment
culture
Anaxos (GE)
sea
beach
peaceful
water sports
C4 (N=57) C6 (N=72)
18. ENTER 2018 Research Track Slide Number 18
Approach
• Linear model
• Step wise feature selection
• 80/20 training & test split
• Fitting 7 models, one for each factor
• Assess Performance
– R2
– RMSE
19. ENTER 2018 Research Track Slide Number 19
Results
R2
RMSEtrain RMSEtest
Sun & Chill-Out 0.70 0.21 0.25
Knowledge & Travel 0.66 0.18 0.20
Independence & History 0.56 0.19 0.20
Culture & Indulgence 0.58 0.22 0.25
Social & Sports 0.19 0.16 0.18
Action & Fun 0.76 0.16 0.19
Nature & Recreation 0.52 0.20 0.23
20. ENTER 2018 Research Track Slide Number 20
Sun & Chill-Out
Coefficient Std. Error Signif.
(Intercept) 0.229 0.027 ***
sea 0.425 0.040 ***
beach & swim 0.302 0.052 ***
health resort 0.271 0.045 ***
sightseeing -0.232 0.036 ***
nature & landscape 0.145 0.032 ***
lake 0.184 0.050 ***
nightlife -0.250 0.051 ***
a neurotic sun lover, who likes warm weather and
sun bathing and does not like cold, rainy or crowded
places
21. ENTER 2018 Research Track Slide Number 21
Results cnt.
• Statistically significant relationship
• On average 8 attributes in use
• Both types (motivational & geographical)
• In more than one model:
– sightseeing, culture, entertainment, family,
quietness, gastronomy, and image & flair
– health resort, and winter sports resort
23. ENTER 2018 Research Track Slide Number 23
Conclusions
• Evidence that there is a relation between
destination attributes and the Seven-Factors
• Some attributes are more determinant
• Seven-Factors are well described through the
models (on average 58% of the variance)
Feasible & Scalable approach to determine the
Seven-Factors
24. ENTER 2018 Research Track Slide Number 24
Future Work
• Performance Evaluation of different
methods (Ridge, RandomForest etc. )
• Similar analysis for accommodations
• Comprehensive data model of tourism
products
32. ENTER 2018 Research Track Slide Number 32
List of used Attributes
• Motivational ratings:
sight seeing, culture, entertainment, family, quietness,
gastronomy, image & flair, beach & swim, nature &
landscape, nightlife, sports, hiking, winter sports, sailing,
mobility, mountain biking, golf, and shopping
• Geographical attributes:
sea, health resort, winter sports resort, lake, old town,
metropolis, and mountains
33. ENTER 2018 Research Track Slide Number 33
List of not used Attributes
• Motivational ratings:
wellness, price level, accommodations, scuba diving, kite
& windsurfing, cycling, horseback riding, and gays
• Geographical attributes:
island, sandy beach, forest, river, desert, pebble beach,
sand & pebble beach, hill, swamp, volcano, fjord, flat
decaying sand beach, beach promenade, wine-growing,
and heath
Welcome everyone!
First of all, let me introduce my self.
My name is Mete Sertkan and I am a Master student of Business Informatics at the Vienna university of technology.
I am going to talk about “Mapping of Tourism Destinations to Travel Behavioural Patterns”.
My co-authors are Julia Neidhardt and Professor Hannes Werthner, who are also my Master thesis supervisors.
I will begin with a short introduction to show you the motivation behind this study and the problem we want to solve.
This will be followed by the State of the Art, to give an overview ”what is already out there”
Afterwards I will introduce you the data we are working with
Then I will show you how we build our models that should solve our problem and the results
Finally some conclusion are driven and Future Work is shown
About 65% of leisure travellers start researching online before they make a travel decision.
Where to go? How to travel? What to do?
Research has shown that especially in the early phase of decision making , people have difficulties in expressing their preferences and needs explicitly.
This constitutes a big issue for key-word based tools, since the user does not know what to type in.
Recommender System are helping here out and are trying to lead the user to the right product.
We se that an important function of Recommender Systems is to help people to make better decisions.
Personality plays an important role in decision making.
Hence, more and more research is focused in personality based recommenders.
The user can be captured via:
- Big Five , more general model not only for the tourism domain
- Predefined Tourist Roles, like 17 Tourist Roles of Gibson and Yiannakis
- Seven-Factor-Model
SUN CHILLOUT: a neurotic sun lover, who likes warm weather and sun bathing and does not like cold, rainy or crowded places;KNOWLEDGE: an open minded, educational and well-organized mass tourist, who likes traveling in groups and gaining knowledge, rather than being lazy;
INDEPENDENCE: an independent mass tourist, who is searching for the meaning of life, is interested in history and tradition, and likes to travel independently, rather than organized tours and travels;CULTURE: an extroverted, culture and history loving high-class tourist, who is also a connoisseur of good food and wine;
SOCIAL: an open minded sportive traveller, who loves to socialize with locals and does not likes areas of intense tourism;ACTION: a jet setting thrill seeker, who loves action, party, and exclusiveness and avoids quiet and peaceful places;
NATURE: a nature and silence lover, who wants to escape from everyday life and avoids crowded places and large cities.
For Users: a picture based approach
Simple picture selection proces to determine a users profile within the seven factors
Gamified way wich is expierenced as inspiring and exiting by users
Avoid tedious questionnaires and the cold start problem
In order to recommend tourism products, they have also to be represented in the same space as the user to enable to calculate some distance metric
In the proposed solution experts mapped &gt;10.000 tourism products manually
Here is the Key Idea of the paper.
On the left side we a tourism destination. In this case Dahab in Egypt. Which is described through its attributes.
On the right side the Seven-Factors are represented.
What we trying to do is, to find and explain associations between attributes and the Seven-Factors to enable an automated mapping of tourism destination onto the Seven-Factors.
The first two papers form the bases of our paper. Here the Seven-Factor Model and the picture based approach is introduced.
In contrast to that in this work we consider destinations as tourism products, there are POIs like activities, events etc. were considered.
Use of Travel… : In this paper it is shown that it is reasonable to use predefined personality roles to capture a users profile
Recommender systems: nice overview of recommendation techniques and challenges recommender systems are facing
Personality and recommenders systems: shows the importance of personality in recommender systems and the importance of designing and evaluating in this sense in a user centric way. And also the difference of explicit and implicit acquisition of personality