A Novel Recommender System for helping Marathoners to Achieve a new Personal-Best
|Title:||A Novel Recommender System for helping Marathoners to Achieve a new Personal-Best||Authors:||Smyth, Barry; Cunningham, Pádraig||Permanent link:||http://hdl.handle.net/10197/9053||Date:||31-Aug-2017||Online since:||2017-11-28T12:47:55Z||Abstract:||We describe a novel application for recommender systems -- helping marathon runners to run a new personal-best race-time -- by predicting a challenging, but achievable target-time, and by recommending a tailored race-plan to achieve this time. A comprehensive evaluation of prediction accuracy and race-plan quality is provided using a large-scale dataset with almost 400,000 runners from the last 12 years of the Chicago marathon.||Funding Details:||Science Foundation Ireland||Funding Details:||Insight Research Centre||Type of material:||Journal Article||Publisher:||ACM||Start page:||116||End page:||120||Copyright (published version):||2017 ACM||Keywords:||Recommender Systems||DOI:||10.1145/3109859.3109874||Language:||en||Status of Item:||Peer reviewed||Is part of:||RecSys '17 Proceedings of the Eleventh ACM Conference on Recommender Systems||Conference Details:||RecSys '17 the Eleventh ACM Conference on Recommender Systems, Como, Italy, 27-31 August 2017||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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