Abstract
Climbing is a popular sport and recreational activity. Unfortunately, there is a lack of technologies for supporting climbers in choosing what climbing route to climb next. We introduce a project aimed at developing a Climbing Recommender System for suggesting routes that are suited for training and practicing sport climbing. We model a climber by relying on both explicit and implicit feedback. Implicit feedback is acquired by an automatic activity recognition component (in climbing gyms), while explicit feedback is acquired by means of a mobile application. We also present a recommendation approach based on the prediction of the subjective evaluation of climbing routes’ difficulty. In fact, often climbers perceive the difficulty of a route differently from the official grade. The prediction method is based on the analysis of how climbers deviate their assessment of routes’ difficulty from the official difficulty grade, and it generates explanations for the predictions.