Climber Behavior Modeling and Recommendation

Ivanova, I. 2021. Climber Behavior Modeling and Recommendation. 29th Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2021). Online from Utrecht, the Netherlands. 21 - 25 Jun 2021 ACM. https://doi.org/10.1145/3450613.3459658

TitleClimber Behavior Modeling and Recommendation
AuthorsIvanova, I.
TypeConference paper
Abstract

Sport climbing is becoming more and more popular, even among non-specialists. While new routes are built each year, both indoor and outdoor, there is no effective tool for supporting climbers to choose the most appropriate routes, either for training or simply enjoying. Route recommendation is hard and risky because a reliable evaluation of the climber’s capabilities, status and subjective difficulty perception is necessary. This can be achieved only with the exploitation of Internet of Things (IoT) sensors for the automatic recording of climbers’ activity. In this research, we want to further extend the still young research subject of activity recognition in sport climbing and combine this with new recommender systems (RSs) techniques for route suggestion. We have developed an initial solution for unobtrusively and automatically detecting climbers’ activities in a gym, and we are now connecting this information with the manual inserted diary data of climbers by means of a mobile application. We present the design and the open research questions for a system that leverages sensor data and explicit feedback to generate a climber’s profile and recommend suitable routes.

Year2021
Conference29th Conference on User Modeling, Adaptation and Personalization (ACM UMAP 2021)
PublisherACM
Publication dates
Published21 Jun 2021
Journal citationpp. 298-303
Book titleUMAP '21: Proceedings of the 29th ACM Conference on User Modeling, Adaptation and Personalization
Digital Object Identifier (DOI)https://doi.org/10.1145/3450613.3459658

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