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Map and Content-Based Climbing Recommender System

Published: 04 July 2022 Publication History

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).

Supplementary Material

MP4 File (video1649709030.mp4)
Video presentation for the prototype of a demo paper "Map and Content-Based Recommender System". In this video, we explain the motivation for the work, state-of-the-art recommender systems developed for similar sports, the concept of an electronic climbing guidebooks. Then we show the usability study results applied via System Usability Scale test for the first version version of the prototype. Furthermore, we show the second version of the prototype developed after the first usability study. As a conclusion, we show the usability study results applied for the prototype.
MP4 File (video1649709030.mp4)
Video presentation for the prototype of a demo paper "Map and Content-Based Recommender System". In this video, we explain the motivation for the work, state-of-the-art recommender systems developed for similar sports, the concept of an electronic climbing guidebooks. Then we show the usability study results applied via System Usability Scale test for the first version version of the prototype. Furthermore, we show the second version of the prototype developed after the first usability study. As a conclusion, we show the usability study results applied for the prototype.
MP4 File (video1649709030.mp4)
Video presentation for the prototype of a demo paper "Map and Content-Based Recommender System". In this video, we explain the motivation for the work, state-of-the-art recommender systems developed for similar sports, the concept of an electronic climbing guidebooks. Then we show the usability study results applied via System Usability Scale test for the first version version of the prototype. Furthermore, we show the second version of the prototype developed after the first usability study. As a conclusion, we show the usability study results applied for the prototype.
MP4 File (video1649709030.mp4)
Video presentation for the prototype of a demo paper "Map and Content-Based Recommender System". In this video, we explain the motivation for the work, state-of-the-art recommender systems developed for similar sports, the concept of an electronic climbing guidebooks. Then we show the usability study results applied via System Usability Scale test for the first version version of the prototype. Furthermore, we show the second version of the prototype developed after the first usability study. As a conclusion, we show the usability study results applied for the prototype.

References

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27crags.com. 2022. Rock Climbing Guide | 27 Crags. Retrieved May 13, 2022 from https://27crags.com
[2]
Marina Andric, Iustina Ivanova, and Francesco Ricci. 2021. Climbing Route Difficulty Grade Prediction and Explanation. In IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. 285–292.
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Aaron Bangor, Philip Kortum, and James Miller. 2009. Determining what individual SUS scores mean: Adding an adjective rating scale. Journal of usability studies 4, 3 (2009), 114–123.
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Matthias Braunhofer, Mehdi Elahi, and Francesco Ricci. 2014. Usability assessment of a context-aware and personality-based mobile recommender system. In International conference on electronic commerce and web technologies. Springer, 77–88.
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Iustina Ivanova. 2022. Climbing recommender system in Arco, Italy: official repository. Retrieved May 13, 2022 from https://github.com/yustiks/Arco- climbing-recommender
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Iustina Ivanova. 2022. Content-Based Climbing Recommender System in Arco, Italy. Retrieved May 16, 2022 from http://climbing-recommender.inf.unibz.it:8080/login
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Iustina Ivanova, Marina Andrić, and Francesco Ricci. 2022. Content-Based Recommendations for Crags and Climbing Routes. In ENTER22 e-Tourism Conference. Springer, 369–381.
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Felix Kosmalla, Florian Daiber, and Antonio Krüger. 2015. ClimbSense: Automatic Climbing Route Recognition Using Wrist-Worn Inertia Measurement Units. In Proceedings of the 33rd Annual ACM Conference on Human Factors in Computing Systems (Seoul, Republic of Korea) (CHI ’15). 10 pages.
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Jeffrey Robert Lean. 2010. A comparative study of interactive rockclimbing guidebooks and conventional hardcopy guidebooks. Ph. D. Dissertation. Queensland University of Technology.
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Benedikt Loepp and Jürgen Ziegler. 2018. Recommending running routes: framework and demonstrator. In Workshop on Recommendation in Complex Scenarios.
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Harold Richins, Sydney Johnsen, John S Hull, 2016. Overview of mountain tourism: Substantive nature, historical context, areas of focus. Mountain tourism: Experiences, communities, environments and sustainable futures (2016), 1–12.
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Jeff Sauro. 2011. A practical guide to the system usability scale: Background, benchmarks & best practices. Measuring Usability LLC.
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Manuel Senettin and Thomas Hofer. 2019. Arco: sport climbing guidebook 2019. Vertical-Life, Brixen.
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Vertical-Life. 2022. 8a.nu | Climbing Areas & Crags. Retrieved May 13, 2022 from https://www.8a.nu
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Jesús Vías, José Rolland, María Luisa Gómez, Carmen Ocaña, and Ana Luque. 2018. Recommendation system to determine suitable and viable hiking routes: a prototype application in Sierra de las Nieves Nature Reserve (southern Spain). Journal of Geographical Systems 20, 3 (2018), 275–294.
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Marc Wilkes and Krzysztof Janowicz. 2008. A graph-based alignment approach to similarity between climbing routes. In Proc. First International Workshop on Information Semantics and its Implications for Geographic Analysis (ISGA). Citeseer.
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Cited By

View all
  • (2024)Sports recommender systems: overview and research directionsJournal of Intelligent Information Systems10.1007/s10844-024-00857-w62:4(1125-1164)Online publication date: 1-Aug-2024
  • (2023)Climbing crags repetitive choices and recommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610652(1158-1164)Online publication date: 14-Sep-2023
  • (2023)Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and ClimbingHuman-Centric Intelligent Systems10.1007/s44230-023-00033-33:3(344-365)Online publication date: 18-Jul-2023

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Information & Contributors

Information

Published In

cover image ACM Conferences
UMAP '22 Adjunct: Adjunct Proceedings of the 30th ACM Conference on User Modeling, Adaptation and Personalization
July 2022
409 pages
ISBN:9781450392327
DOI:10.1145/3511047
Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 04 July 2022

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Author Tags

  1. recommender system
  2. sport climbing
  3. usability study

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  • Demonstration
  • Research
  • Refereed limited

Funding Sources

  • EFRE-FESR programme 2014-2020

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UMAP '22
Sponsor:

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Overall Acceptance Rate 162 of 633 submissions, 26%

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UMAP '25

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Cited By

View all
  • (2024)Sports recommender systems: overview and research directionsJournal of Intelligent Information Systems10.1007/s10844-024-00857-w62:4(1125-1164)Online publication date: 1-Aug-2024
  • (2023)Climbing crags repetitive choices and recommendationsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610652(1158-1164)Online publication date: 14-Sep-2023
  • (2023)Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and ClimbingHuman-Centric Intelligent Systems10.1007/s44230-023-00033-33:3(344-365)Online publication date: 18-Jul-2023

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