Presentation at UMAP 2020

1. HOW DOES PERSONALITY INFLUENCE
PREFERENCES FOR TOURIST
ATTRACTIONS?
MODELING TOURISTS’ PERSONALITY IN
RECOMMENDER SYSTEMS
Patrícia Alves, Pedro Saraiva, João Carneiro
Pedro Campos, Helena Martins, Paulo Novais
Goreti Marreiros
ACM UMAP 2020. 28th Conference on User Modeling, Adaptation and Personalization. 12-18th July, 2020. Online from Genoa, Italy
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2. AGENDA
2

3. 3

4. PERSONALITY
AND
RECOMMENDER
SYSTEMS
4

5. PERSONALITY
AND
RECOMMENDER
SYSTEMS
5
Personalization became the main concern for the
effectiveness of RS
To know personal information about the user is
crucial for building a robust profile
Consideration of other research areas such as
Psychology
Psychological aspects, such as personality, moods
and emotions, are being perceived to influence the
variance in the user preferences and behavior in RS
Evidence of better
results than generic
approaches
Background

6. STSGroup (Nguyen and
Ricci, 2018)
Trip Coach (Gretzel
et al., 2004)
PixMeAway (Neidhardt
et al., 2015)
Background
6

7. PERSONALITY
“Personality is the sum-total of the actual or
potential behavior-patterns of the organism,
as determined by heredity and environment”
Eysenck (1998)
Each individual has behavior patterns, which
are considered relatively stable over time in
different situations
7
Background

8. 8
Background
Five Factor Model (adapted from Costa and MacCrae, 1992)

9. PERSONALITY
 Strongly related to the users’ preferences
 Users with similar personalities tend to choose
similar items or contents
 Evidenced to improve the recommendations
made to groups and can even help in the cold-
start problem
 Correlating the users’ personalities and their
preferences can help find the preferences of
users with similar personalities
 The more information about the tourist is known
better recommendations can be made
9
Background

10. HOW IS
PERSONALITY
RELATED TO
PREFERENCES FOR
TOURIST
ATTRACTIONS?
10

11. PSYCHOLOGY
OF TOURISM
Psychological aspects are related to
the choice of specific destinations
Tourist typologies based on
psychological aspects
Research of relationships among
personality traits and tourist behaviors
or preferences
Background
11

12. PSYCHOLOGY OF TOURISM
12
Tourist Typologies
Population curve of Plog’s psychographic groups
(adapted from (S. C. Plog, 1974))
The four tourist types proposed by Cohen (1972)
Travel-related personality types (Gretzel et al., 2004)

13. PSYCHOLOGY OF TOURISM
13
Hard
adventure
travel
Interest in
cultural
experiences
Need for arousal
(excitement
seeking)
Need for
material
resources
Soft
adventure
travel
Interest in cultural
experiences
Competitiveness
Schneider and Vogt
(2012)
Background

14. 14
Bujisic, Bilgihan, and Smith (2015)
Low Openness to
Experience
High Openness
to Experience
Aesthetic and
escapist experiences
Entertainment and
educational
experiences
High Extraversion
Educational and
escapist experiences
Background
PSYCHOLOGY OF TOURISM

15. PSYCHOLOGY
OF TOURISM:
LIMITATIONS
15
Most studies are mainly descriptive of tourists’ behavior
(Typologies of Tourists)
Do not predict how tourists’ behavior impacts the choice of
tourist preferences
The ones predicting how psychological aspects influence
tourist behavior or preferences mostly rely on Sensation
Seeking and/or Extraversion scales
Few studies try to correlate all Big Five dimensions to
tourist behaviors or preferences
Background

16. PSYCHOLOGY
OF TOURISM:
LIMITATIONS
Tourist typologies do not fully match the
tourists’ preferences for tourist attractions
Many different combinations of intensity for the
personality traits exist
A single typology may not be enough for a
certain tourist
Not all attractions present in a typology may be
suitable for a tourist
Background
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17. METHODOLOGY
17

18. METHODOLOGY
Study of terms for classifying the most significant tourist attractions
Online questionnaire to gather:
- socio-demographic data (26 items)
- personality characteristics (Big Five Inventory, 44-items)
- personal preferences and concerns when travelling (34
items)
- travel motivations (adapted from Pearce and Lee (2005),
28 items)
- preferences for tourist attractions (68 items)
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19. METHODOLOGY
Responses collected during 4 months from Portuguese individuals from
the academic sector and their respective relatives/friends (n = 508)
Data analysed using:
Exploratory Factor Analysis (EFA)
Confirmatory Factor Analysis (CFA)
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20. RESULTS AND ANALYSIS
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21. 161; 32%
347; 68%
Sex
Male
Female
0
10
20
30
40
50
60
70
80
-50 50 150 250 350 450 550
Age
Sample descriptive statistics (n=508)
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22. 1
109
9
2
10
191
122
64
0 50 100 150 200 250
Primary/elementary school
Secondary/High school
Technological Specialization Course (TSC)
Professional Higher Technical Course (CTESP)
Bachelor degree (2-3 years)
Degree (3-5 years)
Master degree
Doctorate degree
(blank)
Education level
Sample descriptive statistics (n=508)
22

23. 27
273
12
11
41
2
132
3
1
0 50 100 150 200 250 300
Education Sciences
Exact and Engineering Sciences
Financial, Administrative and Commercial
Hotel, Restaurants and Tourism
Life and Health Sciences
Natural and Environmental Sciences
Social Sciences and Humanities
Sport
None
Area(s) of formation
Sample descriptive statistics (n=508)
23

24. 107; 21%
222; 44%
155; 31%
19; 4%
How many different countries you visited so far?
11 or more
4 to 10
3 or less
Never visited other countries
Sample descriptive statistics (n=508)
24

25. 1; 0%23; 5%
400; 79%
83; 16%
1; 0%
How often do you travel abroad in leisure per year?
7 to 10 times
4 to 6 times
3 times or less
Never
4 times or less
(blank)
Sample descriptive statistics (n=508)
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26. 389
119
0
50
100
150
200
250
300
350
400
450
No Yes
Have you ever lived in different countries?
Sample descriptive statistics (n=508)
26

27. PREFERENCES FOR TOURIST
ATTRACTIONS
27

28. Results and Analysis
A1 Go to a Gastronomy Festival (food and/or drinks)
A2 Watch a natural phenomenon (e.g., volcanic eruption or northern lights)
A3 Watch a religious celebration
A4 Visit the historic cities/villages of the destination
A5 Visit an oceanarium
A6 Visit caves/caverns/volcanoes
A7 Visit archaeological sites / ruins
A8 Attend cultural activities / artistic performances
A9 Go to the disco/nightclub
A10 Appreciate natural landscapes
A11 Do hiking / mountaineering
A12 Practice aquatic sports (e.g., sailing, canoeing, diving, jet skiing)
A13 Go to a theme park (e.g., Disneyland Paris)
A14
Undergo health and wellness treatments (e.g., hydrotherapy centers, mineral water
resorts)
A15 Go to a Zoo
A16
Attend a typical celebration of the destination (e.g., popular celebrations, carnivals,
fireworks)
A17 Go to a film festival
A18 Taste typical local dishes
A19 Visit a botanical garden
A20
Visit monuments (e.g. churches, cathedrals, castles, fortresses, monasteries, palaces,
etc.)
A21 Visit a beach for its natural beauty
A22 Go to the beach (sunbathing/ swimming)
A23 To enjoy / buy local handicrafts
A24 Ride a bike
A25 Go to a funfair (e.g., amusements such as Ferris wheel, bumper cars, etc.)
A26 Attend gyms / fitness centers
A27 Go to a water park
A28 Go to a SPA / beauty center
A29 Do motorsports (e.g., karting, motocross)
A30 Have a picnic
A31 To go shopping / see storefronts (window shopping)
A32 Visit museums of historical themes
A33 Visit museums of scientific themes (e.g., planetarium, paleontology)
A34 Visit viewpoints of natural landscape
A35 Visit viewpoints of urban landscape
A36 Visit nature or wildlife reserves
A37 Observe sub-aquatic environments / marine life (e.g., snorkeling, submarine)
A38 Visit large man-made constructions (e.g., bridges, tunnels, mines)
A39 Go to a thematic parade (e.g., military, electronic music)
A40 Participate in a gastronomy tour (typical and/or gourmet dishes, wine tasting)
A41 Walk in the forest / woods
A42 Take a walk along the river / sea coast
A43 Go to a music festival/concert
A44 Go to a dance/ballet festival
A45 Go to balls (dancing)
A46 Practice climbing or bungee jumping
A47 Visit mountain areas / gorges
A48 Go to a live music bar/place
A49 Take boat trips to know the destination's coast
A50 Take boat trips for the historical value of the route
A51 Take boat trips for the pleasure of boating
A52 Take a walk in a city park
A53 Play ball sports (e.g., football, handball, volleyball, tennis)
A54 Do a safari
A55 Play at the casino
A56 Assist to a sporting competition (e.g., watch a football game from a club of that country)
A57 Ride a horse
A58 Hunt / fish
A59 Participate in an escape game
A60 Watch a bullfight
A61 Go to the circus
A62 Go on a cruise
A63 Do air sports (e.g., parachute jump, skydiving, gliding)
A64 Go to the swimming pool to swim/dive
A65 Go to the swimming pool to relax
A66 Have vacation on an island
A67 Assist an opera/theater
A68 Ski
Tourist attractions
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29. Factor Item Description Estimated correlations α
Adventure
F1
A46 Practice climbing or bungee jumping 0,775
0.876
A63 Do air sports (e.g., parachute jump, skydiving, gliding) 0,758
A68 Ski 0,750
A12 Practice aquatic sports (e.g., sailing, canoeing, diving, jet skiing) 0,744
A29 Do motorsports (e.g., karting, motocross) 0,627
A37 Observe sub-aquatic environments / marine life (e.g., snorkeling, submarine) 0,575
Nature
F2
A41 Walk in the forest / woods 0,821
0.857
A11 Do hiking / mountaineering 0,721
A42 Take a walk along the river / seacoast 0,662
A47 Visit mountain areas / gorges 0,635
A36 Visit nature or wildlife reserves 0,632
A10 Appreciate natural landscapes 0,625
A6 Visit caves/caverns/volcanoes 0,523
Entertainment &
Nightlife
F3
A44 Go to a dance/ballet festival 0,823
0.849
A43 Go to a music festival/concert 0,743
A45 Go to balls (dancing) 0,722
A48 Go to a live music bar/place 0,683
A17 Go to a film festival 0,624
A67 Assist to an opera/theater 0,621
A8 Attend cultural activities / artistic performances 0,512
A9 Go to the disco/nightclub 0,473
Sun, Water &
Sand
F4
A65 Go to the swimming pool to relax 0,797
0.807
A22 Go to the beach (sunbathing/ swimming) 0,725
A66 Have vacation on an island 0,648
A64 Go to the swimming pool to swim/dive 0,642
Museums &
Landscapes
F5
A32 Visit museums of historical themes 0,741
0.775
A34 Visit viewpoints of natural landscape 0,731
A33 Visit museums of scientific themes (e.g., planetarium, paleontology) 0,670
A35 Visit viewpoints of urban landscape 0,637
Varimax rotated component matrix for the proposed tourist attractions, showing the factors F1-F5 extracted using EFA,
the estimated correlations between the items and factors, and each factors Cronbach’s Alpha reliability
Results and Analysis
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30. Theme &
Animal Parks
F6
A15 Go to a Zoo 0,821
0.736A13 Go to a theme park (e.g., Disneyland Paris) 0,641
A27 Go to a water park 0,590
Cultural
Heritage
F7
A7 Visit archaeological sites / ruins 0,737
0.763A20 Visit monuments (e.g. churches, cathedrals, castles, fortresses, monasteries, palaces, etc.) 0,720
A4 Visit the historic cities/villages of the destination 0,657
Sports & Games
F8
A56 Assist to a sporting competition (e.g., watch a football game from a club of that country) 0,764
0.723
A55 Play at the casino 0,638
A53 Play ball sports (e.g., football, handball, volleyball, tennis) 0,601
A59 Participate in an escape game 0,572
Gastronomy
F9
A1 Go to a Gastronomy Festival (food and/or drinks) 0,844
0.742A40 Participate in a gastronomy tour (typical and/or gourmet dishes, wine tasting) 0,772
A18 Taste typical local dishes 0,674
Boat Tours
F10
A51 Take boat trips for the pleasure of boating 0,626
0.790A49 Take boat trips to know the destination’s coast 0,619
A50 Take boat trips for the historical value of the route 0,555
Health & Well-
being
F11
A28 Go to a SPA / beauty center 0,582
0.733A14 Undergo health and wellness treatments (e.g., hydrotherapy centers, mineral water resorts) 0,575
A26 Attend gyms / fitness centers 0,556
Varimax rotated component matrix for the proposed tourist attractions, showing the factors F6-F11 extracted using EFA,
the estimated correlations between the items and factors, and each factors Cronbach’s Alpha reliability
Results and Analysis
30

31. Model obtained after the CFA and EFA procedures, showing the normalized regression weights for each item
(simplified model, disturbances and covariances omitted) - factors regression weights statistically significant
in the prediction of their respective items for p < 0,001*** (two-tailed)
χ2/df=3.046; CFI= 0.828; GFI= 0.787; PCFI= 0.737, PGFI=
0.672, RMSEA= 0.064; p[rmsea≤0.05]p< 0.001
Tourist Attractions Preference model
31

32. PERSONALITY AND PREFERENCES FOR
TOURIST ATTRACTIONS
32

33. Mean SD
Min/Min
range
Max/Max
range
Extraversion 24 6 10/8 40/40
Agreeableness 33 5 14/9 45/45
Conscientiousness 32 5 11/9 45/45
Neuroticism 25 6 10/8 39/40
Openness 37 6 18/10 50/50
Personality dimensions mean total scores (BFI-44), n=508
Results and Analysis
33

34. Distribution of the
five personality
dimensions scores
among the sample
Results and Analysis
34

35. Results and Analysis
Simplified Structural Equation
Model for the proposed
“Personality-Tourist Attractions
Preference” model. For readability,
only the statistically significant
values are presented
(*p < 0.05 (2-tailed), **p < 0.01 (2-
tailed), ***p < 0.001 (2-tailed))
χ2/df= 2.486; CFI= 0.712; GFI= 0.680;
PCFI= 0.676, PGFI= 0.632, RMSEA= 0.054;
p[rmsea≤0.05]p< 0.001
Personality-Tourist Attractions Preference Model
35

36. Results and Analysis
Adventure
Agreeableness
Conscientiousness
+
-
NatureAgreeableness
+
Entertainment
& Nightlife
Agreeableness
Conscientiousness
+
-
Extraversion
Neuroticism
Openness
+
+
+
Sun,
Water &
Sand
Extraversion
Openness
+
+
Neuroticism
+
PERSONALITY VS PREFERENCES FOR TOURIST ATTRACTIONS
36

37. Results and Analysis
PERSONALITY VS PREFERENCES FOR TOURIST ATTRACTIONS
37
Museums &
Landscapes
Openness
Extraversion
-
+
Theme &
Animal
Parks
Agreeableness
Extraversion
+
+
Neuroticism
+
Openness
-
Cultural
Heritage
Agreeableness
Conscientiousness
+
+
Openness
Extraversion
-
+

38. Results and Analysis
PERSONALITY VS PREFERENCES FOR TOURIST ATTRACTIONS
38
Sports &
Games
Openness
Conscientiousness
-
-
Extraversion
Agreeableness
+
+
Gastronomy
Extraversion
Agreeableness
+
+
Boat Tours
Openness
Agreeableness
+
+
Health &
Well-being
Agreeableness
Extraversion
+
+
Openness
-

39. Strong relation
between the five
personality
dimensions
01
Preferences for
specific tourist
attractions can be
predicted by the
tourists’ personality
02
Can be used in RS
for tourism to
automatically
model the users’
preferences
03
39

40. DISCUSSION AND FUTURE WORK
40

41.  Dissemination of the questionnaire to a larger and
diverse sample, with other education levels and areas
 Improvement of the “Personality-Tourist Attractions
Preference” model
 Some relationships found between personality and
tourist attractions can be culture specific
 Conduct the same study on different cultures and
compare the results
41
Discussion and Future Work

42.  To focus only on the five personality dimensions and
not on the more granular thirty traits and on the
correlations between them, seems to limit the
preferences prediction
 Which of the 30 personality traits can be inferred from the BFI?
 Personality structure varies substantially across
cultures
 Certain personality traits are leaner to show different
emotions according to the social context they are in
 To know the users’ personality is not enough
42
Discussion and Future Work

43.  Development of the models to relate Personality to
travel motivations and preferences/concerns for
travelling
43
Discussion and Future Work
It seems undeniable that personality plays an
important role in the kind of tourist attractions one
chooses to visit

44. HOW DOES PERSONALITY INFLUENCE
PREFERENCES FOR TOURIST
ATTRACTIONS?
MODELING TOURISTS’ PERSONALITY IN
RECOMMENDER SYSTEMS
Patrícia Alves, Pedro Saraiva, João Carneiro
Pedro Campos, Helena Martins, Paulo Novais
Goreti Marreiros
ACM UMAP 2020. 28th Conference on User Modeling, Adaptation and Personalization. 12-18th July, 2020. Online from Genoa, Italy
THANK YOU
AND STAY SAFE!
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