Content-Based Recommendations for Crags and Climbing Routes

Ivanova, Iustina; Andrić, Marina; Ricci, Francesco

doi:10.1007/978-3-030-94751-4_33

Cited by 5 publications

(2 citation statements)

References 13 publications

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“…Furthermore, it still needs to model the contextual information of a user and content knowledge of the place. A recent RS was developed by Ivanova et al ( 2022a ), who employed climbers' comments for crags recommendations based on the collaborative filtering approach: the suggestions are supplied as a ranked list based on the number of routes within the crag similar to those liked by the user in the past. Such a system learns users' tastes for route features from their e-logs and offers an interactive PE interface to adjust those.…”

Section: Related Workmentioning

confidence: 99%

“…We asked them to find several crags for their planned climbing vacation to Arco, utilizing three existing systems. Figure 3 shows interface of these systems as follows: (1) 27crags.com , which was selected as one of the most frequently used by the other climbers for crag search: its interface supports users inserting certain constraints for crag features (see Figure 3A ); (2) climbing crag RS based on the content of the climbing routes Content-Based RS (Ivanova et al, 2022a ), which was developed based on the 27crags.com ( Figure 3B ), and the proposed system Visit & Climb (Ivanova and Wald, 2023 ) ( Figure 3C ). The systems use the same information about climbing crags, and they have similar map-based functionality to show crag information, with the difference in the interface of the PE panel and the recommendation algorithms.…”

Section: Experimental Studymentioning

confidence: 99%

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Climbing crags recommender system in Arco, Italy: a comparative study

Ivanova,

Wald

2023

Front. Big Data

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Outdoor sport climbing is popular in Northern Italy due to its vast amount of rock climbing places (such as crags). New climbing crags appear yearly, creating an information overload problem for tourists who plan their sport climbing vacation. Recommender systems partly addressed this issue by suggesting climbing crags according to the most visited places or the number of suitable climbing routes. Unfortunately, these methods do not consider contextual information. However, in sport climbing, as in other outdoor activities, the possibility of visiting certain places depends on several contextual factors, for instance, a suitable season (winter/summer), parking space availability if traveling with a car, or the possibility of climbing with children if traveling with children. To address this limitation, we collected and analyzed the crag visits in Arco (Italy) from an online guidebook. We found that climbing contextual information, similar to users' content preferences, can be modeled by a correlation between recorded visits and crags features. Based on that, we developed and evaluated a novel context-aware climbing crags recommender system Visit & Climb, which consists of three stages as follows: (1) contextual information and content tastes are learned automatically from the users' logs by computing correlation between users' visits and crags' features; (2) those learned tastes are further made adjustable in a preference elicitation web interface; (3) the user receives recommendations on the map according to the number of visits made by a climber with similar learned tastes. To measure the quality of this system, we performed an offline evaluation (where we calculated Mean Average Precision, Recall, and Normalized Discounted Cumulative Gain for top-N), a formative study, and an online evaluation (in a within-subject design with experienced outdoor climbers N = 40, who tried three similar systems including Visit & Climb). Offline tests showed that the proposed system suggests crags to climbers accurately as the other classical models for top-N recommendations. Meanwhile, online tests indicated that the system provides a significantly higher level of information sufficiency than other systems in this domain. The overall results demonstrated that the developed system provides recommendations according to the users' requirements, and incorporating contextual information and crag characteristics into the climbing recommender system leads to increased information sufficiency caused by transparency, which improves satisfaction and use intention.

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Section: Related Workmentioning

confidence: 99%

Section: Experimental Studymentioning

confidence: 99%

Climbing crags recommender system in Arco, Italy: a comparative study

Ivanova,

Wald

2023

Front. Big Data

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Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing

Ivanova¹,

Wald

2023

Hum-Cent Intell Syst

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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.

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Sensor-Based Activity Recognition and Performance Assessment in Climbing: A Review

2022

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In the past decades, a number of technological developments made it possible to continuously collect various types of sport activity data in an unobtrusive way. Machine learning and analytical methods have been applied to flows of sensor data to predict the conducted sport activity as well as to calculate key performance indicators. In that scenario, researchers started to be interested in leveraging pervasive information technologies for sport climbing, thus allowing, in day-to-day climbing practice, the realization of systems for automatic assessment of a climber's performance, detection of injury risk factors, and virtual coaching. This article surveys recent research works on the recognition of climbing activities and the evaluation of climbing performance indicators, where data have been acquired with accelerometers, cameras, force sensors, and other types of sensors. We describe the main types of sensors and equipment adopted for data acquisition, the techniques used to extract relevant features from sensor data, and the methods that have been proposed to identify the activities performed by a climber and to calculate key performance indicators. We also present a classification taxonomy of climbing activities and climbing performance indicators with the aim to unify the existing work and facilitate comparison of different methods. Moreover, open problems that call for new approaches and solutions are here discussed. We conclude that there is considerable scope for further work, particularly in the application of recognition techniques to problems involving different climbing activities. We hope that this survey will assist in the translation of research effort into intelligent environments that climbers will benefit from.

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Content-Based Recommendations for Crags and Climbing Routes

Cited by 5 publications

References 13 publications

Climbing crags recommender system in Arco, Italy: a comparative study

Climbing crags recommender system in Arco, Italy: a comparative study

Recommender Systems for Outdoor Adventure Tourism Sports: Hiking, Running and Climbing

Sensor-Based Activity Recognition and Performance Assessment in Climbing: A Review

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