Mapping of Tourism Destinations to Travel Behavioural Patterns
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This document presents research on mapping tourism destinations to travel behavioral patterns using a seven-factor model. The researchers analyzed data on over 30,000 destinations to build linear models that predict each of the seven factors based on destination attributes. Their models achieved R-squared values between 0.19-0.76, indicating attributes like sights, culture and natural features can effectively predict traveler personality types. Future work could evaluate other modeling techniques and expand the analysis to accommodations.
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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
- 3. ENTER 2018 Research Track Slide Number 3 INTRODUCTION & STATE OF THE ART
- 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)
- 9. ENTER 2018 Research Track Slide Number 9 THE DATA
- 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)
- 17. ENTER 2018 Research Track Slide Number 17 MODEL BUILDING
- 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
- 22. ENTER 2018 Research Track Slide Number 22 CONCLUSION & FUTURE WORK
- 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
- 25. ENTER 2018 Research Track Slide Number 25 Thank you! Questions?
- 26. ENTER 2018 Research Track Slide Number 26 Knowledge & Travel Coefficient Std. Error t-statistic p-value Signif. (Intercept) 0.066 0.015 4.47 0.000 *** sightseeing 0.427 0.034 12.47 0.000 *** culture 0.275 0.038 7.19 0.000 *** entertainment 0.330 0.052 6.34 0.000 *** family -0.162 0.040 -4.09 0.000 *** sea -0.091 0.022 -4.21 0.000 ***
- 27. ENTER 2018 Research Track Slide Number 27 Independence & History Coefficient Std. Error t-statistic p-value Signif. (Intercept) 0.274 0.020 13.48 0.000 *** culture 0.451 0.039 11.48 0.000 *** sightseeing 0.176 0.038 4.60 0.000 *** sea -0.120 0.023 -5.24 0.000 *** quietness -0.092 0.027 -3.37 0.000 *** gastronomy 0.135 0.051 2.65 0.008 ** winter sports resort -0.143 0.055 -2.61 0.009 **
- 28. ENTER 2018 Research Track Slide Number 28 Culture & Indulgence Coefficient Std. Error t-statistic p-value Signif. (Intercept) 0.112 0.021 5.28 0.000 *** culture 0.382 0.054 7.12 0.000 *** sightseeing 0.204 0.045 4.57 0.000 *** gastronomy 0.282 0.062 4.57 0.000 *** image & flair 0.169 0.042 4.05 0.000 *** family -0.138 0.048 -2.88 0.004 ** sea -0.070 0.025 -2.86 0.004 **
- 29. ENTER 2018 Research Track Slide Number 29 Social & Sports Coefficient Std. Error t-statistic p-value Signif. (Intercept) 0.464 0.018 26.37 0.000 *** old town -0.086 0.034 -2.51 0.012 * sports 0.425 0.084 5.05 0.000 *** sightseeing -0.142 0.036 -3.97 0.000 *** hiking 0.081 0.023 3.50 0.000 *** entertainment -0.205 0.080 -2.57 0.010 *
- 30. ENTER 2018 Research Track Slide Number 30 Action & Fun Coefficient Std. Error t-statistic p-value Signif. (Intercept) -0.032 0.016 -1.97 0.049 * metropolis 0.171 0.047 3.64 0.000 *** winter sports 0.350 0.062 5.61 0.000 *** sailing 0.177 0.041 4.36 0.000 *** sea 0.137 0.021 6.58 0.000 *** quietness -0.113 0.027 -4.13 0.000 *** entertainment 0.627 0.049 12.82 0.000 *** family -0.360 0.053 -6.82 0.000 *** mobility 0.164 0.054 3.02 0.002 ** sightseeing 0.116 0.035 3.34 0.000 *** mountain biking 0.075 0.028 2.68 0.007 ** golf 0.107 0.038 2.82 0.005 ** winter sports resort 0.136 0.055 2.50 0.012 *
- 31. ENTER 2018 Research Track Slide Number 31 Nature & Recreation Coefficient Std. Error t-statistic p-value Signif. (Intercept) 0.723 0.027 26.86 0.000 *** gastronomy -0.345 0.057 -5.87 0.000 *** quietness 0.438 0.028 15.59 0.000 *** sightseeing -0.172 0.041 -4.17 0.000 *** shopping -0.344 0.057 -6.03 0.000 *** mountains 0.137 0.040 3.41 0.000 *** health resort 0.140 0.044 3.16 0.001 ** image & flair -0.094 0.032 -2.91 0.003 **
- 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
- 34. ENTER 2018 Research Track Slide Number 34 Dist. of Seven-Factors in C1-6
- 35. ENTER 2018 Research Track Slide Number 35 Sun & Chill-Out by Experts
- 36. ENTER 2018 Research Track Slide Number 36 Sun & Chill-Out predicted
Editor's Notes
- 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