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  5. Running with Cases: A CBR Approach to Running Your Best Marathon
 
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Running with Cases: A CBR Approach to Running Your Best Marathon

Author(s)
Smyth, Barry  
Cunningham, Pádraig  
Uri
http://hdl.handle.net/10197/9164
Date Issued
2017-06-21
Date Available
2018-01-11T13:00:07Z
Abstract
Every year millions of people around the world train for, and compete in, the marathon. As race-day approaches, and training schedulesbegin to wind down, many participants will turn their attention totheir race strategy, as they strive to achieve their best time. To help withthis, in this paper we describe a novel application of case-based reasoningto address the dual task of: (1) predicting a challenging, but achievable,personal best race-time for a marathon runner; and (2) recommendinga race-plan to achieve this time. We describe how suitable cases can begenerated from pairs of race histories and how we can predict a personalbest race-time and produce a tailored race-plan by reusing the race historiesof similar runners. This work is evaluated using data from the lastsix years of the London Marathon.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Publisher
Springer
Series
Lecture Notes in Computer Science
Copyright (Published Version)
2017 Springer
Subjects

Recommender systems

Case-based reasoning

Sports analytics

DOI
10.1007/978-3-319-61030-6_25
Language
English
Status of Item
Peer reviewed
Journal
Lecture Notes in Computer Science, vol 10339
ISBN
978-3-319-61029-0
This item is made available under a Creative Commons License
https://creativecommons.org/licenses/by-nc-nd/3.0/ie/
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insight_publication.pdf

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Owning collection
Insight Research Collection

Item descriptive metadata is released under a CC-0 (public domain) license: https://creativecommons.org/public-domain/cc0/.
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