13 Pictures as a tool for matching tourist preferences with destinations
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Wilfried Grossmann
Abstract
Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.
Abstract
Usually descriptions of touristic products comprise information about accommodation, tourist attractions or leisure activities. Tourist decisions for a product are based on personal characteristics, planned vacation activities and specificities of potential touristic products. The decision should guarantee a high level of emotional and physical well-being, considering also some hard constraints like temporal and monetary resources, or travel distance. The starting point for the design of the described recommender system is a unified description of the preferences of the tourist and the opportunities offered by touristic products using the so-called seven-factor model. For the assignment of the values in the seven-factor model a predefined set of pictures is the pivotal instrument. These pictures represent various aspects of the personality and preferences of the tourist as well as general categories for the description of destinations, i. e., certain tourist attractions like landscape, cultural facilities, different leisure activities or emotional aspects associated with tourism. Based on the picture selection of a customer a so-called factor algorithm calculates values for each factor of the seven-factor model. This is a rather fast and intuitive method for acquisition of information about personality and preferences. The evaluation of the factors of the products is obtained by mapping descriptive attributes of touristic products onto the predefined pictures and afterwards applying the factor algorithm to the pictures characterizing the product. Based on this unified description of tourists and touristic products a recommendation can be defined by measuring the similarity between the user attributes and the product attributes. The approach is evaluated using data from a travel agency. Furthermore, other possible applications are discussed.
Kapitel in diesem Buch
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
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Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
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Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
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Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
Kapitel in diesem Buch
- Frontmatter I
- Introduction V
- Contents IX
- List of Contributing Authors XI
-
Part I: Foundations of personalization
- 1 Theory-grounded user modeling for personalized HCI 1
- 2 User-centered recommender systems 33
- 3 Fairness of information access systems 59
-
Part II: User input and feedback
- 4 Personalization and user modeling for interaction processes 81
- 5 How to use socio-emotional signals for adaptive training 99
- 6 Explanations and user control in recommender systems 129
- 7 Feedback loops and mutual reinforcement in personalized interaction 153
-
Part III: Personalization purposes and goals
- 8 Personalizing the user interface for people with disabilities 175
- 9 Personalized persuasion for behavior change 205
- 10 Personalization approaches for remote collaborative interaction 237
-
Part IV: Personalization domains
- 11 Listener awareness in music recommender systems: directions and current trends 279
- 12 Tourist trip recommendations – foundations, state of the art and challenges 313
- 13 Pictures as a tool for matching tourist preferences with destinations 337
- Index 355
