Sport climbing has recently gained large popularity among tourists as a recreational activity. Many people are interested to climb the most beautiful rock climbing places around the world. This has pushed the creation of a large number of climbing routes, to accommodate more and more enthusiasts. However, climbers are not facilitated in their search of routes to climb with any advanced tool, especially in outdoor climbing: they are only provided with either printed or electronic guidebooks, which cannot generate recommendations based on the user’s preferences. Well-tailored climbing routes recommendations have a potential interest for all the involved stakeholders: the users and the companies providing the route information in the form of websites, or guidebooks. To this end, we propose a Content-based Climbing Recommender System prototype. An initial usability study based on the Software Usability Scale (SUS) proves the first version of the prototype to be well-designed (obtained SUS score of 71.6), and the updated version of a system addressing usability problems received an excellent evaluation score (SUS score is 89.3).

Map and Content-Based Climbing Recommender System

Buriro A.;
2022-01-01

Abstract

Sport climbing has recently gained large popularity among tourists as a recreational activity. Many people are interested to climb the most beautiful rock climbing places around the world. This has pushed the creation of a large number of climbing routes, to accommodate more and more enthusiasts. However, climbers are not facilitated in their search of routes to climb with any advanced tool, especially in outdoor climbing: they are only provided with either printed or electronic guidebooks, which cannot generate recommendations based on the user’s preferences. Well-tailored climbing routes recommendations have a potential interest for all the involved stakeholders: the users and the companies providing the route information in the form of websites, or guidebooks. To this end, we propose a Content-based Climbing Recommender System prototype. An initial usability study based on the Software Usability Scale (SUS) proves the first version of the prototype to be well-designed (obtained SUS score of 71.6), and the updated version of a system addressing usability problems received an excellent evaluation score (SUS score is 89.3).
2022
Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10278/5065201
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