攀爬路线难度等级预测与解释

Marina Andric, I. Ivanova, Francesco Ricci
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引用次数: 2

摘要

这篇文章的重点是运动攀岩和创新的工具的设计,以支持登山者浏览和搜索路线攀登。一条路线的难度和等级通常由专业登山者评估,他们被称为路线设定者。一个经常攀岩的人,在尝试了一条路线后,可能会觉得它比路线设定者或多或少地困难。估计登山者对路线的感知难度是很重要的,这样可以建议登山者期望的目标感知难度的路线。我们开发了一种基于知识的方法,使用领域特定的特征来训练预测模型。此外,该问题被建模为推荐系统中的评级预测任务,使用矩阵分解方法和自定义归一化解决方案。基于知识的方法使我们能够开发一个等级预测解释功能。在离线实验中,我们在基线上演示了改进。此外,我们还展示了如何在一家为运动攀岩市场提供信息服务的大公司开发的应用程序中利用所提出的技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Climbing Route Difficulty Grade Prediction and Explanation
This article focuses on sport climbing and on the design of innovative tools for supporting climbers to browse and search routes to climb. The difficulty of a route, its grade, is normally assessed by expert climbers, named route setters. A regular climber, after trying a route, may perceive it more or less difficult than the route setter. It is important to estimate this climber’s perceived difficulty of the routes in order to suggest the routes that have a target perceived difficulty as expected by the climber. We develop a knowledge-based approach that uses domain-specific features to train a predictive model. Additionally, the problem is modeled as a rating prediction task in a recommender system, using a matrix factorization approach with a custom normalization solution. The knowledge-based approach enables us to develop a grade prediction explanation functionality. In off-line experiments, we demonstrate improvements over a baseline. Moreover, we show how the proposed techniques can be exploited in an app developed by a major company offering information services to the sport climbing market.
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