Introducing Context in Climbing Crags Recommender System in Arco, Italy

Ivanova, Iustina; Wald, Mike

doi:10.1145/3581754.3584120

Cited by 5 publications

(4 citation statements)

References 8 publications

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“…For example, such information as sun orientation toward the crag informs climbers to avoid visiting a sunny crag in summer because it can become too hot for climbing there. This issue was partly solved in the novel CARS by Ivanova and Wald ( 2023 ), describing the main advantage of contextual preference elicitation. However, this system lacks a detailed assessment (formative study and online and offline evaluation analysis).…”

Section: Related Workmentioning

confidence: 99%

“… Three systems' interfaces were given to climbers to find crags in Arco, Italy: (A) www.27crags.com ; (B) based on the content of the climbing routes (Content-Based RS) (Ivanova et al, 2022b ); (C) based on the visits of the previous climbers (Visit & Climb) (Ivanova and Wald, 2023 ). …”

Section: Experimental Studymentioning

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%

“…One of the standard methods is to infer the context using data mining methods from implicit feedback, e.g., the number of user visits to a specified place (Jannach et al, 2018 ). Those techniques have been partially explored in a recent system (Ivanova and Wald, 2023 ), which allows to adjust the user's contextual preferences. However, this system needs a complete study (formative, online, and offline) with its stakeholders (outdoor climbers).…”

Section: Introductionmentioning

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%

Section: Experimental Studymentioning

confidence: 99%

Section: Introductionmentioning

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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Sports recommender systems: overview and research directions

Felfernig,

Wundara,

Tran

et al. 2024

J Intell Inf Syst

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Sports recommender systems receive an increasing attention due to their potential of fostering healthy living, improving personal well-being, and increasing performances in sports. These systems support people in sports, for example, by the recommendation of healthy and performance-boosting food items, the recommendation of training practices, talent and team recommendation, and the recommendation of specific tactics in competitions. With applications in the virtual world, for example, the recommendation of maps or opponents in e-sports, these systems already transcend conventional sports scenarios where physical presence is needed. On the basis of different examples, we present an overview of sports recommender systems applications and techniques. Overall, we analyze the related state-of-the-art and discuss future research directions.

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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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Introducing Context in Climbing Crags Recommender System in Arco, Italy

Cited by 5 publications

References 8 publications

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

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

Sports recommender systems: overview and research directions

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

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