Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing
Abstract Adventure tourism is a popular and growing segment within the tourism industry that involves, but is not limited to, hiking, running, and climbing activities. These activities attract investment from foreign travelers interested in practicing sports while exploring other countries. As a res...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Springer Nature
2023-07-01
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| Series: | Human-Centric Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s44230-023-00033-3 |
| _version_ | 1797636826189529088 |
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| author | Iustina Ivanova Mike Wald |
| author_facet | Iustina Ivanova Mike Wald |
| author_sort | Iustina Ivanova |
| collection | DOAJ |
| description | Abstract Adventure tourism is a popular and growing segment within the tourism industry that involves, but is not limited to, hiking, running, and climbing activities. These activities attract investment from foreign travelers interested in practicing sports while exploring other countries. As a result, many software companies started developing Artificial Intelligence solutions to enhance tourists’ outdoor adventure experience. One of the leading technologies in this field is recommender systems, which provide personalized recommendations to tourists based on their preferences. While this topic is actively being researched in some sports (running and hiking), other adventure sports disciplines have yet to be fully explored. To standardize the development of intelligence-based recommender systems, we conducted a systematic literature review on more than a thousand scientific papers published in decision support system applications in three outdoor adventure sports, such as running, hiking, and sport climbing. Hence, the main focus of this work is, firstly, to summarize the state-of-the-art methods and techniques being researched and developed by scientists in recommender systems in adventure tourism, secondly, to provide a unified methodology for software solutions designed in this domain, and thirdly, to give further insights into open possibilities in this topic. This literature survey serves as a unified framework for the future development of technologies in adventure tourism. Moreover, this paper seeks to guide the development of more effective and personalized recommendation systems. |
| first_indexed | 2024-03-11T12:40:45Z |
| format | Article |
| id | doaj.art-7c159ce88f6f41f9bc956acbf3563cd3 |
| institution | Directory Open Access Journal |
| issn | 2667-1336 |
| language | English |
| last_indexed | 2024-03-11T12:40:45Z |
| publishDate | 2023-07-01 |
| publisher | Springer Nature |
| record_format | Article |
| series | Human-Centric Intelligent Systems |
| spelling | doaj.art-7c159ce88f6f41f9bc956acbf3563cd32023-11-05T12:20:12ZengSpringer NatureHuman-Centric Intelligent Systems2667-13362023-07-013334436510.1007/s44230-023-00033-3Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and ClimbingIustina IvanovaMike Wald0School of Electronics and Computer Science, University of SouthamptonAbstract Adventure tourism is a popular and growing segment within the tourism industry that involves, but is not limited to, hiking, running, and climbing activities. These activities attract investment from foreign travelers interested in practicing sports while exploring other countries. As a result, many software companies started developing Artificial Intelligence solutions to enhance tourists’ outdoor adventure experience. One of the leading technologies in this field is recommender systems, which provide personalized recommendations to tourists based on their preferences. While this topic is actively being researched in some sports (running and hiking), other adventure sports disciplines have yet to be fully explored. To standardize the development of intelligence-based recommender systems, we conducted a systematic literature review on more than a thousand scientific papers published in decision support system applications in three outdoor adventure sports, such as running, hiking, and sport climbing. Hence, the main focus of this work is, firstly, to summarize the state-of-the-art methods and techniques being researched and developed by scientists in recommender systems in adventure tourism, secondly, to provide a unified methodology for software solutions designed in this domain, and thirdly, to give further insights into open possibilities in this topic. This literature survey serves as a unified framework for the future development of technologies in adventure tourism. Moreover, this paper seeks to guide the development of more effective and personalized recommendation systems.https://doi.org/10.1007/s44230-023-00033-3Adventure tourismRecommender systemsArtificial intelligenceSports |
| spellingShingle | Iustina Ivanova Mike Wald Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing Human-Centric Intelligent Systems Adventure tourism Recommender systems Artificial intelligence Sports |
| title | Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing |
| title_full | Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing |
| title_fullStr | Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing |
| title_full_unstemmed | Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing |
| title_short | Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing |
| title_sort | recommender systems for outdoor adventure tourism sports hiking running and climbing |
| topic | Adventure tourism Recommender systems Artificial intelligence Sports |
| url | https://doi.org/10.1007/s44230-023-00033-3 |
| work_keys_str_mv | AT iustinaivanova recommendersystemsforoutdooradventuretourismsportshikingrunningandclimbing AT mikewald recommendersystemsforoutdooradventuretourismsportshikingrunningandclimbing |